diff options
78 files changed, 2767 insertions, 4 deletions
@@ -1,6 +1,27 @@ # Custom 3rdparty/ +# Project specific + +webpack-stats.dev.json +#static +3rdparty/ +_local/ +*db.sqlite3 +__pycache__ +deploy_settings.py +#static +s3cfg +.DS_Store +node_modules/ + +client_secret.json +sheets.googleapis.com-python.json + +# ------------------------------------------ +# GitHub Python .gitignore ruleset +# ------------------------------------------ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -11,13 +32,15 @@ __pycache__/ # Distribution / packaging .Python + +env/ build/ develop-eggs/ -dist/ +#dist/ downloads/ eggs/ .eggs/ -lib/ +#lib/ lib64/ parts/ sdist/ @@ -84,7 +107,7 @@ celerybeat-schedule # SageMath parsed files *.sage.py -# Environments +# Environment .env .venv env/ @@ -105,3 +128,30 @@ venv.bak/ # mypy .mypy_cache/ + +backend/backend/ +backend/media +backend/static/* + +webpack-stats.dev.json + +*.swp + +!sis/static/ +sis/static/img/* +sis/static/feature/* +sis/static/uploaded/* + +*.pkl + +.git-old/ +data_store/ + +config/settings/production.py + +old-package.json + +datasets/ + +*.tar + @@ -1,3 +1,36 @@ # MegaPixels -FaceQuery.me, mozilla, nytimes
\ No newline at end of file +FaceQuery.me, mozilla, nytimes + +# megapixels dev + +## installation + +``` +pip install urllib3 +pip install bs4 +pip install http +``` + +## ascii + +``` +xxxxdddd5xxdddd5555vvvv~xxxxdddd55vvvv7777~~~~xxxxdddddd5555vvvv7777 +xxxxdddd5xxdddd55v~xxxxddddddddd55vvvv7777~~~~xxxxdddd5555v5vvvv7777 +xxxxdddd555vvvv77~~xxxxdddddddd55vvvv77777~~~xxxxddd55555v5vvvv77777 +aaaaxddd5555vvvv77xxxxddddddddd55vvvvv77777~~xxxxddd55555v5vvvvddddd +aaaaayyMMMMMqqqqddeeeexxxxxxdddd55vvvv77777~~xxaayyyyMMMMMqMqqqvdddd +aaaaayyMMMMMqqqqqddeeeaaaaaayyyyMMqqqqqddddeeaaaayyyyMMMMMqMqqqqdddd +ccccaDD%%%%%##qqqddeeaaaaaayyyyMMqqqqddddeeeeaaaayyyyMMMMMqMqqqq@@@@ +ccccc%%%###qqqd#@@eeeaaaaaayyyyMMqqqqddddeeeeacccDDDD%%%%%#%###q@@@@ +cccx%%%%###i##@#@eeeeaccccccDyyyM%##qddd@@eeeeccccDDyMMMMqMqqqq@@@@@ +llcccDDDD%%%%###@eeeecccccccDDDD%%####@@@@eeeecccDDD%%%%#%###q@&&&&& +lllllSDDD%%%%###@llllcccccccDDDD%%####@@@@eeeecclSSD%%%%#%####@&&&&& +llllSSSSQQ%%%##&&&lllllcclllSDDD%%####@@@@eellcllSSD%%%%#%####&&&&&& +aallSSSSQQQQQ%%&&&llllllllllSSSD%Q%%##@@&&llllllllSSQQQQ%Q%%%#&&rrrr +aaaaiiiiVVQQQ%%drrrrrrrrllllSSSSQQ%%%%&&&&llllllllSSSQQQQ%Q%%%%&rrrr +aaaaiiiiVVVVVdddrrrrrrrraaaaiSSSQVdd%%&&rrrrrraaaaiiSQQQQ%Q%%%%rrrrr +xxxxdddd55VVVddv7777~~~~aaaaiiiiVVddddrrrrrrrraaaaiiiiVVVVdVdddd7777 +xxxxdddd5555vvvv7777~~~~xxxxdiiiV555vvvv7777~~~~xxxxdddd5555vvvv7777 +``` +>>>>>>> 0dc3e40434c23e4d48119465f39b03bf35fb56bd diff --git a/__init__.py b/__init__.py new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/__init__.py diff --git a/check-counts.py b/check-counts.py new file mode 100644 index 00000000..4fed4494 --- /dev/null +++ b/check-counts.py @@ -0,0 +1,39 @@ +import os +import sys +import csv +from math import ceil +import subprocess +import random + +import click + +@click.command() +def check_counts(): + """Split a CSV into groups.""" + mypath = './datasets/scholar/entries/' + onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))] + recs = [] + for f in onlyfiles: + with open(os.path.join(mypath, f), 'rb') as f: + reader = csv.reader(f, delimiter='|') + print f + print repr(reader) + lines = list(reader) + rec = lines[0] + recs.append(rec) + + out_fn = './datasets/scholar_entries.csv' + write_csv(out_fn, keys=None, chunk=recs) + +# Write a CSV +def write_csv(fn, keys, chunk): + print(fn) + with open(fn, 'w') as f: + writer = csv.writer(f) + if keys is not None: + writer.writerow(keys) + for row in chunk: + writer.writerow(row) + +if __name__ == '__main__': + check_counts() diff --git a/content-script.crx b/content-script.crx Binary files differnew file mode 100644 index 00000000..f6619fc9 --- /dev/null +++ b/content-script.crx diff --git a/content-script.pem b/content-script.pem new file mode 100644 index 00000000..d6575905 --- /dev/null +++ b/content-script.pem @@ -0,0 +1,28 @@ +-----BEGIN PRIVATE KEY----- +MIIEvwIBADANBgkqhkiG9w0BAQEFAASCBKkwggSlAgEAAoIBAQDBsc12oytNxMAo +tqlRp6xfD+lz40LAZfiWK1c26KzB0CVb+ywYVPeJx37tW1pK8A8olAHrAvQ4N/No +xKi91X4z+mrewvAcmBSgAHHtCNnX8WOSxlE3wY+AmqLzUFZXmCGIz6AbkcxlMiLd +fAB0VLm1x9PspQo/N7M/S5JLsf744x64+sLDyggVBl9m6YMNQssmGYTew5q91FRb +f3t5v/HuLMHCAO8izyfxhiFd43ZLy1ZmpMq3pb0IDG4bHOdrL/YIwVN67pKhrca/ +aAFh4cf5gMljWaSAtFD6bIBRBYkGb5JfeXCmrBa0FMOfbbUon9wn9PkGHvBFmK/I +kUkFu+EDAgMBAAECggEAAd+wdydY8eWnXj12Id4XYswKnB5ktOvHndxJebSCh1V2 +Jw+k+49G3cRaS/2IQBVWcDQOWLGsnyabajYM09lO4T3Y7tK/8+bcvDeE4qAlS1uA +dRkKDBoJQ1s7nFL4otxL5sDrcZjVe10izTW342g2a7XFLRLjqHzndQNMJQNRziKQ +AIw7v565FNK59Wo5QI6gBGIGf0lxR7wclJSbZ57yhPeqloHSEL1EmQH06P930zxT +93XYdPrAMjGKDtgRJjGXR9lqr7rJ2VkVJlgq4Z+xdZnJ07p69AZNsVQJ6RWWzRza +Y0xijoUA/DxL1Ft8M2ZP9LqguHOV0Yz4Jqfcg17rcQKBgQD7M5JSAeNYU497//jO +FxuxzKTtHK2XEGVtn3O6QaptWe1V4velyoCEyV1LziQmWol1qMKvM/mmFWc+vv7B +sEyBj4lJdPNKeUGT44SKSIEIOnYEqiZg/NqfI5NOHJyIoSCuD/ZyWgumNbM6oR42 +ZOH3i3CJb8u7s8LdgO3dR8jWRQKBgQDFZQKRHpTWs9A2hlym6uV0MqzEc8akt16f +Y4qMooqPHKb4UYvhv/IIzK4d24Gawyn5DeiZ+EhIos12RXSPaqPefchgxzP7YjEL +hPJQa4Sm/yTXfh+bVh5zzhLXTaWdlrWnrn402VJOE0Og9OV6XpaVDIiyLgZghwTH +7szxTR5SpwKBgQCuvsNs1O9oBEjuQ7S6+QxE9U1NvQNs8PT9+0wwJ3n9M++MLaWE +6YfZ15JVJ4OyQo0ZElrMMmXaiU0XMxpZduDhyA4C0Z5lElDyJfWPlLxeEv0CZbLp +CTy0M7VHQ1QJH71yGJyiE83Cw9x4TpiL0kX3REL2+JRLcNN/7y6JHmSx2QKBgQCj +gENcCMSoAlDYmPptl+tvD/pizrMfBHPv+fds/cVfPRBgZstCtka4WzFtsb/sfPNr +Q8YCotDcuV0ONgGSOXZ6eB1dbCL2eiK9s+UyD5zU5kmR5PoxJGgc6/hXNg2UNp1I +wxPumoO9/97ol3DspyR7WOJxFAZcNfoT0Q0YYhRXyQKBgQCiFMV/Ja/G9syhtu4m +ejiVQvsMbvuU5GZN9mEYz/s6POBvFAlK/5gS2x0zPBSdj8VIAVgZiKtX+9/+3vbi +Jq8jqzcoLP6Zjby+brUKTVwc/q1I9aIlyoo+DGH9E9MLszUwCZYYNjG/nLC+jsge +E7lrsKnbHTL6LBsrIBOeOAa3Zw== +-----END PRIVATE KEY----- diff --git a/content-script/.gitignore b/content-script/.gitignore new file mode 100644 index 00000000..5ca0973f --- /dev/null +++ b/content-script/.gitignore @@ -0,0 +1,2 @@ +.DS_Store + diff --git a/content-script/alone-off.png b/content-script/alone-off.png Binary files differnew file mode 100644 index 00000000..677b2273 --- /dev/null +++ b/content-script/alone-off.png diff --git a/content-script/alone-on.png b/content-script/alone-on.png Binary files differnew file mode 100644 index 00000000..7c0f441e --- /dev/null +++ b/content-script/alone-on.png diff --git a/content-script/background.js b/content-script/background.js new file mode 100644 index 00000000..78244b12 --- /dev/null +++ b/content-script/background.js @@ -0,0 +1,135 @@ +// this script talks to the button, tells whether it is off or on, +// and communicates this state to the open tabs + +var on = true; +var TYPES = { Status: 0, Connected: 1, Disconnected: 2, Image: 3, Text: 4 } + +chrome.browserAction.onClicked.addListener(function(tab) { + on = ! on + if (on) { + localStorage.setItem("megapixels", "on") + chrome.browserAction.setIcon({path: 'alone-on.png'}) + chrome.browserAction.setTitle({title: "Stop Mego"}); + chrome.tabs.getSelected(null, function(tab) { + chrome.tabs.sendMessage(tab.id, {method:"start"}, function(response){}) + }) + } + else { + localStorage.setItem("megapixels", "off") + chrome.browserAction.setIcon({path: 'alone-off.png'}) + chrome.browserAction.setTitle({title: "Start Mego"}) + chrome.tabs.getSelected(null, function(tab) { + chrome.tabs.sendMessage(tab.id, {method:"stop"}, function(response){}) + }) + } +}); + +// websocket boilerplate +// var ws, ws_timeout, ws_delay = 500, opt = {} +// function reset () { +// close() +// clearTimeout(ws_timeout) +// ws_delay = Math.min(ws_delay*2, 60000) +// ws_timeout = setTimeout(open, ws_delay) +// } +// function open () { +// close() +// ws = new WebSocket('ws://' + opt.server + ':' + opt.port) +// ws.onopen = function (event) { +// ws_delay = 500 +// send({ type: TYPES.Connected, data: { client: "browser" } }) +// console.log('websocket open') +// } +// ws.onerror = function () { +// reset() +// } +// ws.onclose = function () { +// reset() +// } +// } +// function close () { +// try { +// ws.close() +// ws = null +// } catch (e) {} +// } +// function send (msg, force) { +// msg.name = opt.name +// if ( (on || force) && ws ) ws.send(JSON.stringify(msg)) +// } + +// // load options and connect websocket +// chrome.storage.sync.get({ +// server: 'localhost', +// port: 3000, +// name: 'whomever', +// }, function(items) { +// console.log(items) +// "server port name".split(" ").forEach(function(id){ +// opt[id] = items[id] +// }) +// open() +// }); + +// chrome.storage.onChanged.addListener(function(changes, ns) { +// for (key in changes) { +// var change = changes[key] +// opt[key] = change.newValue +// } +// open() +// }); + +// // listen for image urls +// chrome.webRequest.onResponseStarted.addListener(function(details) { +// send({ type: TYPES.Image, data: details.url }) +// }, { +// urls: ["<all_urls>"], +// types: ["image"] +// }, ["responseHeaders"]); + +function push(s){ + console.log(s) + let d = localStorage.getItem('site') + if (s === 'done') { + download('[' + localStorage.getItem('site') + ']') + return + } + if (d) s = d + ',' + s + localStorage.setItem('site', s) +} +function reset(){ + localStorage.setItem('site', '') +} +function download(content, fileName, contentType) { + var a = document.createElement("a"); + var file = new Blob([JSON.stringify(content)], {type: contentType}); + a.href = URL.createObjectURL(file); + a.download = fileName; + a.click(); +} + +// new pages will check if we're listening +chrome.extension.onMessage.addListener(function(request, sender, sendResponse) { + console.log(request) + switch (request.type) { + case TYPES.Status: + sendResponse({ status: localStorage['megapixels'] }) + break + case TYPES.Text: + push(request.data) + break + } +}); + +// injects check.js into tabs if not already loaded +chrome.tabs.query({}, function(tabs) { + for (var i = 0; i < tabs.length; i++) { + var tabid = tabs[i].id; + if (tabs[i].url.indexOf("chrome") === -1) { + chrome.tabs.executeScript(tabid, { + file: "check.js" + }) + } + } +}) + diff --git a/content-script/check.js b/content-script/check.js new file mode 100644 index 00000000..0644084d --- /dev/null +++ b/content-script/check.js @@ -0,0 +1,102 @@ +(function(){ + + var listening = false, loaded = false + var TYPES = { Status: 0, Connected: 1, Disconnected: 2, Image: 3, Text: 4 } + var PUNCTUATION_REGEX = /[\[\]\{\}]/g // i.e. resembles code + var NULL_ARRAY = [] + var seen = {} + const toArray = (a) => Array.prototype.slice.call(a) + const $ = (s) => document.querySelector(s) + const $$ = (s) => document.querySelectorAll(s) + + function init () { + if (window.location.href.indexOf("lvh.me") !== -1 || window.location.href.indexOf("localhost") !== -1) return + bind() + } + function bind () { + chrome.extension.onMessage.addListener(onMessage) + chrome.extension.sendMessage({ type: TYPES.Status }, gotStatus) + } + function gotStatus (response) { + console.log('got status', response) + if (response && response.status === "on") { + console.log('its on', loaded) + if (! loaded) { + console.log('started') + setTimeout(() => { start() }, 5000) + // send(document.body.innerText) + // setInterval(function(){ + // send(document.body.innerText) + // }, 10000) + } + loaded = true + } + } + function onMessage (request, sender, sendResponse) { + switch (request.method) { + case 'start': + start() + break + case 'stop': + listening = false + break + } + } + function start(){ + console.log(window.location.href, listening) + if (window.location.href.indexOf('schol' + 'ar' + '.go' + 'og' + 'le') === -1) return + if (listening) return + listening = true + energize() + } + + function energize(){ + console.log('energize') + const records = toArray($$(".gs_r")).map((el, i) => { + let data = {} + let link = el.querySelector("h3 a") + if (link) { + data.link = link.href + } + let pdfLink = el.querySelector(".gs_or_ggsm a") + if (pdfLink) { + data.pdfLink = pdfLink.href + } + let attribution = el.querySelector('.gs_a') + if (attribution) { + data.attribution = attribution.innerText + data.attributionLinks = toArray(attribution.querySelectorAll('a')).map(a => ({ + href: a.href, + name: a.innerText, + })) + } + let snippet = el.querySelector('.gs_a') + if (snippet) { + data.snippet = snippet.innerText + } + let citationLink = el.querySelector('.gs_fl a:nth-of-type(3)') + if (citationLink && citationLink.innerText.match('Cited by')) { + data.citationLink = citationLink.href + data.citationCount = parseInt(citationLink.innerText.replace(/^\s*Cited by /, ''), 10) || -1 + } + return data + }) + let record = { + title: document.querySelector('title').innerText, + url: window.location.href, + records: records, + } + send(JSON.stringify(record)) + let nextLink = $("#gs_n td:last-child a") + if (nextLink) { + setTimeout(() => { nextLink.click() }, 19000 + (Math.random() * 21000)) + } + send("done") + } + function send (text) { + chrome.extension.sendMessage({ type: TYPES.Text, data: text }, function(){}) + } + + init() + +})() diff --git a/content-script/icon-128.png b/content-script/icon-128.png Binary files differnew file mode 100644 index 00000000..75ccfc80 --- /dev/null +++ b/content-script/icon-128.png diff --git a/content-script/icon-16.png b/content-script/icon-16.png Binary files differnew file mode 100644 index 00000000..b2bfd098 --- /dev/null +++ b/content-script/icon-16.png diff --git a/content-script/icon-48.png b/content-script/icon-48.png Binary files differnew file mode 100644 index 00000000..b4b752a7 --- /dev/null +++ b/content-script/icon-48.png diff --git a/content-script/index.html b/content-script/index.html new file mode 100644 index 00000000..577ab5b0 --- /dev/null +++ b/content-script/index.html @@ -0,0 +1,8 @@ +<!doctype html> +<html> +<head> +<script type="text/javascript" src="background.js"></script> +</head> +<body> +</body> +</html> diff --git a/content-script/manifest.json b/content-script/manifest.json new file mode 100644 index 00000000..dc7e9c9b --- /dev/null +++ b/content-script/manifest.json @@ -0,0 +1,29 @@ +{ + "name": "Mego", + "version": "1.0.0", + "manifest_version": 2, + "description": "Mego content extension", + "homepage_url": "https://asdf.us/", + "icons": { "16": "icon-16.png", + "48": "icon-48.png", + "128": "icon-128.png" }, + "permissions": ["storage", "tabs", "webRequest", "*://*/*"], + "background": { + "page": "index.html" + }, + "content_scripts": [ + { + "matches": ["*://*/*"], + "js": ["check.js"] + } + ], + "browser_action": { + "name": "Start Mego", + "default_icon": "alone-on.png", + "icons": ["alone-on.png", "alone-off.png"] + }, + "options_ui": { + "page": "options.html", + "chrome_style": true + } +} diff --git a/content-script/options.html b/content-script/options.html new file mode 100644 index 00000000..8b5ba90c --- /dev/null +++ b/content-script/options.html @@ -0,0 +1,16 @@ +<!DOCTYPE html> +<html> +<head> + <title>Alone Together Plugin Options</title> + <style> + body { padding: 10px; } + label { min-width: 90px; display: inline-block; } + #port { width: 50px; } + div { margin-bottom: 10px; } + </style> +</head> + +<body> +</body> + +</html>
\ No newline at end of file diff --git a/content-script/options.js b/content-script/options.js new file mode 100644 index 00000000..ef9396fa --- /dev/null +++ b/content-script/options.js @@ -0,0 +1,34 @@ +// Saves options to chrome.storage.sync. +function $(n) { return document.querySelector(n) } +function save_options() { + chrome.storage.sync.set({ + server: $("#server").value, + port: parseInt( $("#port").value, 10 ), + name: $("#name").value, + }, function() { + // Update status to let user know options were saved. + var status = document.getElementById('status'); + status.textContent = 'Options saved.'; + setTimeout(function() { + status.textContent = ''; + }, 750); + }); +} + +// Restores select box and checkbox state using the preferences +// stored in chrome.storage. +function restore_options() { + chrome.storage.sync.get({ + server: 'localhost', + port: 3000, + name: 'whomever', + }, function(items) { + console.log(items) + "server port name".split(" ").forEach(function(id){ + $("#"+id).value = items[id] + }) + }); +} +document.addEventListener('DOMContentLoaded', restore_options); +document.getElementById('save').addEventListener('click', save_options); +console.log("WHAT") diff --git a/datasets/citations-1.csv b/datasets/citations-1.csv new file mode 100644 index 00000000..f9400fcd --- /dev/null +++ b/datasets/citations-1.csv @@ -0,0 +1,77 @@ +Database Name,Title,Journal/Pub/Conference,Year,Pages,Volume,Author1,Author2,Author3,Author4,Author5,Author 6,PDF,Priority,URL,bibtex_reference_only,notes
+PIE,"The CMU Pose, Illumination, and Expression Database",IEEE Transactions on Pattern Analysis and Machine Intelligence,Dec 2003,"25, No. 12",,T. Sim,S. Baker,M. Bsat,,,,,,http://www.cs.cmu.edu/~simonb/pie_db/pami.pdf,,
+YouTubeFaces,Face Recognition in Unconstrained Videos with Matched Background Similarity,IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),2011,,,Lior Wolf,Tal Hassner,Itay Maoz,,,,,,,,
+Names and Faces,Who's in the Picture ,NIPS,2004,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,David A. Forsyth,,,,2,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_whos_in_the_picture.pdf,,
+RaFD ,Presentation and validation of the Radboud Faces Database,Cognition & Emotion,2010,1377-1388,24.8,"Langner, O.","Dotsch, R."," Bijlstra, G.","Wigboldus, D.H.J.","Hawk, S.T.","van Knippenberg, A.",,,http://dx.doi.org/10.1080/02699930903485076,DOI: 10.1080/02699930903485076,
+MIFS,Spoofing Faces Using Makeup: An Investigative Study,"Proc. of 3rd IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), (New Delhi, India)",2017,,,C. Chen,A. Dantcheva,T. Swearingen,A. Ross,,,,,http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf,,
+MegaFace 2,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Kemelmacher-Shlizerman, Ira","Seitz, Steven M","Miller, Daniel","Brossard, Evan",,,,If you're using or participating in Challenge 1 please cite:,http://megaface.cs.washington.edu/KemelmacherMegaFaceCVPR16.pdf,"@inproceedings{kemelmacher2016megaface, +title={The megaface benchmark: 1 million faces for recognition at scale}, +author={Kemelmacher-Shlizerman, Ira and Seitz, Steven M and Miller, Daniel and Brossard, Evan}, +booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, +pages={4873--4882}, +year={2016} +}",
+CMDP,Distance Estimation of an Unknown Person from a Portrait ,"ECCV 2014, Zurich, Switzerland",2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,http://www.vision.caltech.edu/~mronchi/papers/ECCV14_FaceDistancePortrait_PAPER.pdf,"@incollection{perona2014PortraitDistanceEstimation, + title={Distance Estimation of an Unknown Person from a Portrait}, + author={Xavier P. Burgos-Artizzu, Matteo Ruggero Ronchi and Pietro Perona}, + booktitle={Computer Vision--ECCV 2014}, + pages={313--327}, + year={2014}, + publisher={Springer} +} +",
+MORPH non-commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+CK+,The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression,"Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA",2010,94-101,,"Ambadar, Z.","Cohn, J.F.","Kanade, T.","Lucey, P.","Matthews, I.A.","Saragih, J.M.",,,http://ieeexplore.ieee.org/document/5543262/,"@article{Lucey2010TheEC, + title={The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression}, + author={Patrick Lucey and Jeffrey F. Cohn and Takeo Kanade and Jason M. Saragih and Zara Ambadar and Iain A. Matthews}, + journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops}, + year={2010}, + pages={94-101} +}",
+LFWP,Localizing Parts of Faces Using a Consensus of Exemplars,Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011,,,Peter N. Belhumeur,"David W. Jacobs,",David J. Kriegman,Neeraj Kumar,,,,,http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf,,
+Adience,Age and Gender Estimation of Unfiltered Faces,"Transactions on Information Forensics and Security (IEEE-TIFS), special issue on Facial Biometrics in the Wild",2014,2170 - 2179,9,Eran Eidinger,Roee Enbar, Tal Hassner,,,,,,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,,
+300-W,300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge,"Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,2,,,
+AFW,"Face detection, pose estimation and landmark localization in the wild","Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island,",2012,,,X. Zhu,D. Ramanan,,,,,,,http://www.ics.uci.edu/~xzhu/paper/face-cvpr12.pdf,,
+MsCeleb,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,European Conference on Computer Vision,2016,,,"Guo, Yandong","Zhang, Lei","Hu, Yuxiao","He, Xiaodong","Gao, Jianfeng",,,,https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/MSCeleb-1M-a.pdf,"@INPROCEEDINGS { guo2016msceleb, + author = {Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng}, + title = {M{S}-{C}eleb-1{M}: A Dataset and Benchmark for Large Scale Face Recognition}, + booktitle = {European Conference on Computer Vision}, + year = {2016}, + organization={Springer}}",
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,bianco2017large-age.pdf,,http://www.ivl.disco.unimib.it/activities/large-age-gap-face-verification/,,
+IMDB,DEX: Deep EXpectation of apparent age from a single image,IEEE International Conference on Computer Vision Workshops (ICCVW),Dec 2015,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,2,,"@InProceedings{Rothe-ICCVW-2015, + author = {Rasmus Rothe and Radu Timofte and Luc Van Gool}, + title = {DEX: Deep EXpectation of apparent age from a single image}, + booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)}, + year = {2015}, + month = {December}, +}",
+IMDB,Deep expectation of real and apparent age from a single image without facial landmarks,International Journal of Computer Vision (IJCV),Jul 2016,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,1,,"@article{Rothe-IJCV-2016, + author = {Rasmus Rothe and Radu Timofte and Luc Van Gool}, + title = {Deep expectation of real and apparent age from a single image without facial landmarks}, + journal = {International Journal of Computer Vision (IJCV)}, + year = {2016}, + month = {July}, +}",
+UMD,UMDFaces: An Annotated Face Dataset for Training Deep Networks,Arxiv preprint,2016,,,Ankan Bansal,Anirudh Nanduri,Carlos D Castillo,Rajeev Ranjan,Rama Chellappa,,,1,https://arxiv.org/abs/1611.01484v2,"@article{bansal2016umdfaces, + title={UMDFaces: An Annotated Face Dataset for Training Deep Networks}, + author={Bansal, Ankan and Nanduri, Anirudh and Castillo, Carlos D and Ranjan, Rajeev and Chellappa, Rama} + journal={arXiv preprint arXiv:1611.01484v2}, + year={2016} + }",
+LFW,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.,"University of Massachusetts, Amherst, Technical Report ",2007,07-49,,Gary B. Huang,Manu Ramesh,Tamara Berg,Erik Learned-Miller,,,,,http://vis-www.cs.umass.edu/lfw/lfw.pdf,,various citaton depending on various datasets provided. Citation used here was first one published in 2007
+CelebA,From Facial Parts Responses to Face Detection: A Deep Learning Approach,"in IEEE International Conference on Computer Vision (ICCV),",2015,,,S. Yang,P. Luo,C. C. Loy,X. Tang,,,,,https://arxiv.org/abs/1509.06451,"The following paper employed CelebA for face detection. (linked on the project website) @inproceedings{liu2015faceattributes, + author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang}, + title = {Deep Learning Face Attributes in the Wild}, + booktitle = {Proceedings of International Conference on Computer Vision (ICCV)}, + month = December, + year = {2015} +}",
+UMD,The Do's and Don'ts for CNN-based Face Verification,Arxiv preprint,2017,,,Ankan Bansal,Carlos Castillo,"Rajeev Ranjan,",Rama Chellappa,,,,2,https://arxiv.org/abs/1705.07426,"@article{bansal2017dosanddonts, + title = {The Do's and Don'ts for CNN-based Face Verification}, + author = {Bansal, Ankan and Castillo, Carlos and Ranjan, Rajeev and Chellappa, Rama}, + journal = {arXiv preprint arXiv:1705.07426}, + year = {2017} + }",
+Yale Face Database B,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,PAMI,2001,,,Athinodoros Georghiades,Peter Belhumeur,David Kriegman,,,,,,,,
+CAISA Webface,Learning Face Representation from Scratch,arXiv preprint arXiv:1411.7923.,2014,,,Dong Yi,Zhen Lei, Shengcai Liao,Stan Z. Li,,,,,https://arxiv.org/abs/1411.7923,,
diff --git a/datasets/citations-2.csv b/datasets/citations-2.csv new file mode 100644 index 00000000..ec11b2d0 --- /dev/null +++ b/datasets/citations-2.csv @@ -0,0 +1,89 @@ +Database Name,Title,Journal/Pub/Conference,Year,Pages,Volume,Author1,Author2,Author3,Author4,Author5,Author 6,PDF,Priority,URL,bibtex_reference_only,notes
+COFW,Robust face landmark estimation under occlusion ,"ICCV 2013, Sydney, Australia",2013,,,X. P. Burgos-Artizzu,P. Perona,P. Dollár,,,,,,http://www.vision.caltech.edu/%7Expburgos/papers/ICCV13%20Burgos-Artizzu.pdf,,
+Names and Faces,Names and Faces ,U.C. Berkeley Technical Report,Jan. 2007,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,1,http://www.cs.berkeley.edu/%7Eaberg/papers/journal_berg.pdf,,
+FaceTracer,FaceTracer: A Search Engine for Large Collections of Images with Faces,European Conference on Computer Vision (ECCV),2008,340-353,,N. Kumar,P. N. Belhumeur,S. K. Nayar,,,,,1,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ECCV08.pdf,,
+MIW,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+SVW,Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis,"Proc. International Conference on Automatic Face and Gesture Recognition (FG 2015), Ljubljana, Slovenia",2015,,,Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus,"John Wood,",Dean Craven,,,http://cvlab.cse.msu.edu/pdfs/Morteza_FG2015.pdf," @inproceedings{ sports-videos-in-the-wild-svw-a-video-dataset-for-sports-analysis, + author = { Seyed Morteza Safdarnejad and Xiaoming Liu and Lalita Udpa and Brooks Andrus and John Wood and Dean Craven }, + title = { Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis }, + booktitle = { Proc. International Conference on Automatic Face and Gesture Recognition }, + address = { Ljubljana, Slovenia }, + month = { May }, + year = { 2015 }, +} ",
+LFW-a,,,,,,,,,,,,,,,Comply with any instructions specified for the original LFW data set,
+Helen,Interactive Facial Feature Localization,ECCV,2012,,,Vuong Le,Jonathan Brandt,Zhe Lin,Lubomir Boudev,Thomas S. Huang,,,,http://www.ifp.illinois.edu/~vuongle2/helen/eccv2012_helen_final.pdf,,
+Caltech 10K Web Faces,Pruning Training Sets for Learning of Object Categories,Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2005,,,Anelia Angelova,Yaser Abu-Mostafa,Pietro Perona,,,,,,http://www.vision.caltech.edu/anelia/DataPruning/Angelova05DataPruning.pdf,This is a paper using the dataset (linked on the project website),
+FaceTracer,Face Swapping: Automatically Replacing Faces in Photographs,ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH),2008,,,D. Bitouk,N. Kumar,S. Dhillon,P.N. Belhumeur,S. K. Nayar,,,2,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Bitouk_SIGGRAPH08.pdf,,
+HRT Transgender Database,Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset,"In Proc. of IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems",2013,,,Gayathri Mahalingam,Karl Ricanek Jr.,,,,,,,https://pdfs.semanticscholar.org/b066/733d533250f4ddafd22c12456def7fa24f4c.pdf,,
+MUCT,The MUCT Landmarked Face Database,Pattern Recognition Association of South Africa,2010,,,,S. Milborrow,J. Morkel,F. Nicolls,,,,,http://www.milbo.org/muct/The-MUCT-Landmarked-Face-Database.pdf,"@article{Milborrow10, + author={S. Milborrow and J. Morkel and F. Nicolls}, + title={{The MUCT Landmarked Face Database}}, + journal={Pattern Recognition Association of South Africa}, + year=2010, + note={\url{http://www.milbo.org/muct}} +}",
+AFLW,"Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization",,,,,Martin Koestinger,Paul Wohlhart,Peter M. Roth,Horst Bischof,,,,,https://files.icg.tugraz.at/seafhttp/files/d18813db-78c3-46a9-8614-bc0c8d428114/koestinger_befit_11.pdf,"@INPROCEEDINGS{koestinger11a, + author = {Martin Koestinger, Paul Wohlhart, Peter M. Roth and Horst Bischof}, + title = {{Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization}}, + booktitle = {{Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies}}, + year = {2011} +} ",
+PIPA,Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues,arXiv:1501.05703 [cs.CV],2015,,,Ning Zhang, Manohar Paluri,Yaniv Taigman,Rob Fergus,Lubomir Bourdev,,,,https://arxiv.org/pdf/1501.05703.pdf,"@inproceedings{piper, + Author = {Ning Zhang and Manohar Paluri and Yaniv Taigman and Rob Fergus and Lubomir Bourdev}, + Title = {Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues}, + Eprint = {arXiv:1501.05703}, + Year = {2015}}",
+UCF101,UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,CRCV-TR-12-01,2012,,,"Soomro, K.","Roshan Zamir, A.","Shah, M.",,,,,2,,"@inproceedings{UCF101, + author = {Soomro, K. and Roshan Zamir, A. and Shah, M.}, + booktitle = {CRCV-TR-12-01}, + title = {{UCF101}: A Dataset of 101 Human Actions Classes From + Videos in The Wild}, + year = {2012}}",
+YMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,1,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+FDDB,FDDB: A Benchmark for Face Detection in Unconstrained Settings,"Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts",2010,,,Vidit Jain,Erik Learned-Mille,,,,,,,http://vis-www.cs.umass.edu/fddb/fddb.pdf,"@TechReport{fddbTech, + author = {Vidit Jain and Erik Learned-Miller}, + title = {FDDB: A Benchmark for Face Detection in Unconstrained Settings}, + institution = {University of Massachusetts, Amherst}, + year = {2010}, + number = {UM-CS-2010-009} + }",
+Hipsterwars,Hipster Wars: Discovering Elements of Fashion Styles.,In European Conference on Computer Vision,2014,,,M. Hadi Kiapour,Kota Yamaguchi,Alexander C. Berg,Tamara L. Berg,,,,,http://tamaraberg.com/papers/hipster_eccv14.pdf,"@inproceedings{ + HipsterWarsECCV14, + title = {Hipster Wars: Discovering Elements of Fashion Styles} + author = {M. Hadi Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg}, + booktitle={European Conference on Computer Vision}, + year = {2014} + }",
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,In Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,,,http://www.ivl.disco.unimib.it/download/bianco2017large-age.pdf,,
+CMDP,Distance Estimation of an Unknown Person from a Portrait,ECCV 2014,2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,,,
+Columbia Gaze Data Set,Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction,ACM Symposium on User Interface Software and Technology (UIST),2013,271-280,,B.A. Smith,Q. Yin,S.K. Feiner,S.K. Nayar,,,,,http://www.cs.columbia.edu/~brian/publications/gaze_locking.html,,
+IJB-A,"Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A +",Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2015,1931-1939,07-12-June-2015,"Klare, B. F.","Klein, B.","Taborsky, E.","Blanton, A.","Cheney, J.","Allen, K., ... Jain, A. K.",,,http://ieeexplore.ieee.org/document/7298803/,"DOI: 10.1109/CVPR.2015.7298803 @inbook{882e95bdca414797b4a8e2bfcb5b1fa4, +title = ""Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A"", +abstract = ""Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark."", +author = ""Klare, {Brendan F.} and Ben Klein and Emma Taborsky and Austin Blanton and Jordan Cheney and Kristen Allen and Patrick Grother and Alan Mah and Mark Burge and Jain, {Anil K.}"", +year = ""2015"", +month = ""10"", +doi = ""10.1109/CVPR.2015.7298803"", +isbn = ""9781467369640"", +volume = ""07-12-June-2015"", +pages = ""1931--1939"", +booktitle = ""Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"", +publisher = ""IEEE Computer Society"", + +} +",
+AgeDB,"AgeDB: the first manually collected, in-the-wild age database",Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017,2017,,,S. Moschoglou,A. Papaioannou,C. Sagonas,J. Deng,I. Kotsia, S. Zafeiriou,,,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"@inproceedings{AgeDB, + author = {S. Moschoglou and A. Papaioannou and C. Sagonas and J. Deng and I. Kotsia and S. Zafeiriou}, + address = {Honolulu, Hawaii}, + booktitle = {Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017)}, + month = {June}, + title = {AgeDB: the first manually collected, in-the-wild age database}, + year = {2017}, +}",
+WIDER FACE,WIDER FACE: A Face Detection Benchmark,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou",,,,,,,,,"@inproceedings{yang2016wider, + Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, + Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + Title = {WIDER FACE: A Face Detection Benchmark}, + Year = {2016}}",
diff --git a/datasets/citations-2018310.csv b/datasets/citations-2018310.csv new file mode 100644 index 00000000..68a3ae3e --- /dev/null +++ b/datasets/citations-2018310.csv @@ -0,0 +1,215 @@ +Database Name,Title,Journal/Pub/Conference,Year,Pages,Volume,Author1,Author2,Author3,Author4,Author5,Author 6,PDF,Priority,URL,bibtex_reference_only,notes
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,bianco2017large-age.pdf,,http://www.ivl.disco.unimib.it/activities/large-age-gap-face-verification/,,
+YouTubeFaces,Face Recognition in Unconstrained Videos with Matched Background Similarity,IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),2011,,,Lior Wolf,Tal Hassner,Itay Maoz,,,,,,,,
+LFW,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.,"University of Massachusetts, Amherst, Technical Report ",2007,07-49,,Gary B. Huang,Manu Ramesh,Tamara Berg,Erik Learned-Miller,,,,,http://vis-www.cs.umass.edu/lfw/lfw.pdf,,various citaton depending on various datasets provided. Citation used here was first one published in 2007
+HRT Transgender Database,Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset,"In Proc. of IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems",2013,,,Gayathri Mahalingam,Karl Ricanek Jr.,,,,,,,https://pdfs.semanticscholar.org/b066/733d533250f4ddafd22c12456def7fa24f4c.pdf,,
+JAFFE,Coding Facial Expressions with Gabor Wavelets,3rd IEEE International Conference on Automatic Face and Gesture Recognition,1998,200-205,,Michael J. Lyons,Shigeru Akemastu,Miyuki Kamachi,Jiro Gyoba,,,,,http://www.kasrl.org/fg98-1.pdf,,
+CMDP,Distance Estimation of an Unknown Person from a Portrait,ECCV 2014,2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,,,
+WIDER,Recognize Complex Events from Static Images by Fusing Deep Channels,2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2015,,,"Xiong, Yuanjun and Zhu, Kai and Lin, Dahua and Tang, Xiaoou",,,,,,,,,,
+WIDER FACE,WIDER FACE: A Face Detection Benchmark,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou",,,,,,,,,"@inproceedings{yang2016wider, + Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, + Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + Title = {WIDER FACE: A Face Detection Benchmark}, + Year = {2016}}",
+300-W,300 faces In-the-wild challenge: Database and results,"Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation ""In-The-Wild""",2016,,,C. Sagonas,E. Antonakos,"G, Tzimiropoulos",S. Zafeiriou,M. Pantic,,,1,,,
+300-W,300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge,"Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,2,,,
+300-W,A semi-automatic methodology for facial landmark annotation,"Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2013). Oregon, USA,",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,3,,,
+LFWP,Localizing Parts of Faces Using a Consensus of Exemplars,Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011,,,Peter N. Belhumeur,"David W. Jacobs,",David J. Kriegman,Neeraj Kumar,,,,,http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf,,
+Helen,Interactive Facial Feature Localization,ECCV,2012,,,Vuong Le,Jonathan Brandt,Zhe Lin,Lubomir Boudev,Thomas S. Huang,,,,http://www.ifp.illinois.edu/~vuongle2/helen/eccv2012_helen_final.pdf,,
+Hipsterwars,Hipster Wars: Discovering Elements of Fashion Styles.,In European Conference on Computer Vision,2014,,,M. Hadi Kiapour,Kota Yamaguchi,Alexander C. Berg,Tamara L. Berg,,,,,http://tamaraberg.com/papers/hipster_eccv14.pdf,"@inproceedings{ + HipsterWarsECCV14, + title = {Hipster Wars: Discovering Elements of Fashion Styles} + author = {M. Hadi Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg}, + booktitle={European Conference on Computer Vision}, + year = {2014} + }",
+Adience,Age and Gender Estimation of Unfiltered Faces,"Transactions on Information Forensics and Security (IEEE-TIFS), special issue on Facial Biometrics in the Wild",2014,2170 - 2179,9,Eran Eidinger,Roee Enbar, Tal Hassner,,,,,,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,,
+AFW,"Face detection, pose estimation and landmark localization in the wild","Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island,",2012,,,X. Zhu,D. Ramanan,,,,,,,http://www.ics.uci.edu/~xzhu/paper/face-cvpr12.pdf,,
+AFLW,"Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization",,,,,Martin Koestinger,Paul Wohlhart,Peter M. Roth,Horst Bischof,,,,,https://files.icg.tugraz.at/seafhttp/files/d18813db-78c3-46a9-8614-bc0c8d428114/koestinger_befit_11.pdf,"@INPROCEEDINGS{koestinger11a, + author = {Martin Koestinger, Paul Wohlhart, Peter M. Roth and Horst Bischof}, + title = {{Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization}}, + booktitle = {{Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies}}, + year = {2011} +} ",
+AgeDB,"AgeDB: the first manually collected, in-the-wild age database",Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017,2017,,,S. Moschoglou,A. Papaioannou,C. Sagonas,J. Deng,I. Kotsia, S. Zafeiriou,,,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"@inproceedings{AgeDB, + author = {S. Moschoglou and A. Papaioannou and C. Sagonas and J. Deng and I. Kotsia and S. Zafeiriou}, + address = {Honolulu, Hawaii}, + booktitle = {Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017)}, + month = {June}, + title = {AgeDB: the first manually collected, in-the-wild age database}, + year = {2017}, +}",
+CAISA Webface,Learning Face Representation from Scratch,arXiv preprint arXiv:1411.7923.,2014,,,Dong Yi,Zhen Lei, Shengcai Liao,Stan Z. Li,,,,,https://arxiv.org/abs/1411.7923,,
+Caltech 10K Web Faces,Pruning Training Sets for Learning of Object Categories,Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2005,,,Anelia Angelova,Yaser Abu-Mostafa,Pietro Perona,,,,,,http://www.vision.caltech.edu/anelia/DataPruning/Angelova05DataPruning.pdf,This is a paper using the dataset (linked on the project website),
+CelebA,From Facial Parts Responses to Face Detection: A Deep Learning Approach,"in IEEE International Conference on Computer Vision (ICCV),",2015,,,S. Yang,P. Luo,C. C. Loy,X. Tang,,,,,https://arxiv.org/abs/1509.06451,"The following paper employed CelebA for face detection. (linked on the project website) @inproceedings{liu2015faceattributes, + author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang}, + title = {Deep Learning Face Attributes in the Wild}, + booktitle = {Proceedings of International Conference on Computer Vision (ICCV)}, + month = December, + year = {2015} +}",
+UMD,UMDFaces: An Annotated Face Dataset for Training Deep Networks,Arxiv preprint,2016,,,Ankan Bansal,Anirudh Nanduri,Carlos D Castillo,Rajeev Ranjan,Rama Chellappa,,,1,https://arxiv.org/abs/1611.01484v2,"@article{bansal2016umdfaces, + title={UMDFaces: An Annotated Face Dataset for Training Deep Networks}, + author={Bansal, Ankan and Nanduri, Anirudh and Castillo, Carlos D and Ranjan, Rajeev and Chellappa, Rama} + journal={arXiv preprint arXiv:1611.01484v2}, + year={2016} + }",
+UMD,The Do's and Don'ts for CNN-based Face Verification,Arxiv preprint,2017,,,Ankan Bansal,Carlos Castillo,"Rajeev Ranjan,",Rama Chellappa,,,,2,https://arxiv.org/abs/1705.07426,"@article{bansal2017dosanddonts, + title = {The Do's and Don'ts for CNN-based Face Verification}, + author = {Bansal, Ankan and Castillo, Carlos and Ranjan, Rajeev and Chellappa, Rama}, + journal = {arXiv preprint arXiv:1705.07426}, + year = {2017} + }",
+COFW,Robust face landmark estimation under occlusion ,"ICCV 2013, Sydney, Australia",2013,,,X. P. Burgos-Artizzu,P. Perona,P. Dollár,,,,,,http://www.vision.caltech.edu/%7Expburgos/papers/ICCV13%20Burgos-Artizzu.pdf,,
+CMDP,Distance Estimation of an Unknown Person from a Portrait ,"ECCV 2014, Zurich, Switzerland",2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,http://www.vision.caltech.edu/~mronchi/papers/ECCV14_FaceDistancePortrait_PAPER.pdf,"@incollection{perona2014PortraitDistanceEstimation, + title={Distance Estimation of an Unknown Person from a Portrait}, + author={Xavier P. Burgos-Artizzu, Matteo Ruggero Ronchi and Pietro Perona}, + booktitle={Computer Vision--ECCV 2014}, + pages={313--327}, + year={2014}, + publisher={Springer} +} +",
+FaceTracer,FaceTracer: A Search Engine for Large Collections of Images with Faces,European Conference on Computer Vision (ECCV),2008,340-353,,N. Kumar,P. N. Belhumeur,S. K. Nayar,,,,,1,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ECCV08.pdf,,
+FaceTracer,Face Swapping: Automatically Replacing Faces in Photographs,ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH),2008,,,D. Bitouk,N. Kumar,S. Dhillon,P.N. Belhumeur,S. K. Nayar,,,2,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Bitouk_SIGGRAPH08.pdf,,
+FDDB,FDDB: A Benchmark for Face Detection in Unconstrained Settings,"Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts",2010,,,Vidit Jain,Erik Learned-Mille,,,,,,,http://vis-www.cs.umass.edu/fddb/fddb.pdf,"@TechReport{fddbTech, + author = {Vidit Jain and Erik Learned-Miller}, + title = {FDDB: A Benchmark for Face Detection in Unconstrained Settings}, + institution = {University of Massachusetts, Amherst}, + year = {2010}, + number = {UM-CS-2010-009} + }",
+IMFDB,Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,"National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)",2013,,,Shankar Setty,et al,,,,,,,http://cvit.iiit.ac.in/projects/IMFDB/imfdb.pdf,"@InProceedings{imfdb, +author = {Shankar Setty, Moula Husain, Parisa Beham, Jyothi Gudavalli, Menaka Kandasamy, Radhesyam Vaddi, Vidyagouri Hemadri, J C Karure, Raja Raju, Rajan, Vijay Kumar and C V Jawahar}, +title = {{I}ndian {M}ovie {F}ace {D}atabase: {A} {B}enchmark for {F}ace {R}ecognition {U}nder {W}ide {V}ariations}, +booktitle = {National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)}, +month = {Dec}, +year = {2013} +} ",
+imSitu,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,"(1) Computer Science & Engineering, University of Washington, Seattle, WA +(2) Allen Institute for Artificial Intelligence (AI2), Seattle, WA",,,,Mark Yatskar,Luke Zettlemoyer,Ali Farhadi,,,,,,https://homes.cs.washington.edu/~my89/publications/situations.pdf,,
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,In Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,,,http://www.ivl.disco.unimib.it/download/bianco2017large-age.pdf,,
+LFW-a,,,,,,,,,,,,,,,Comply with any instructions specified for the original LFW data set,
+LFW-a,Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics,"IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 33(10),",2011,,,Lior Wolf,Tal Hassner,Yaniv Taigman,,,,,,http://www.openu.ac.il/home/hassner/projects/Patchlbp/WolfHassnerTaigman_TPAMI11.pdf,,
+MALF,Fine-grained Evaluation on Face Detection in the Wild.,Proceedings of the 11th IEEE International Conference on Automatic Face and Gesture Recognition Conference and Workshops.,2015,,,Bin Yang*,Junjie Yan*,Zhen Lei,Stan Z. Li,,,,,http://www.cbsr.ia.ac.cn/faceevaluation/faceevaluation15.pdf,"@inproceedings{faceevaluation15, +title={Fine-grained Evaluation on Face Detection in the Wild}, +author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z}, +booktitle={Automatic Face and Gesture Recognition (FG), 11th IEEE International +Conference on}, +year={2015}, +organization={IEEE} +}",
+MegaFace 2,Level Playing Field for Million Scale Face Recognition,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017,,,"Nech, Aaron","Kemelmacher-Shlizerman, Ira",,,,,,If you're participating or using data from Challenge 2 please cite:,https://homes.cs.washington.edu/~kemelmi/ms.pdf,"@inproceedings{nech2017level, +title={Level Playing Field For Million Scale Face Recognition}, +author={Nech, Aaron and Kemelmacher-Shlizerman, Ira}, +booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, +year={2017} +}",
+MegaFace 2,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Kemelmacher-Shlizerman, Ira","Seitz, Steven M","Miller, Daniel","Brossard, Evan",,,,If you're using or participating in Challenge 1 please cite:,http://megaface.cs.washington.edu/KemelmacherMegaFaceCVPR16.pdf,"@inproceedings{kemelmacher2016megaface, +title={The megaface benchmark: 1 million faces for recognition at scale}, +author={Kemelmacher-Shlizerman, Ira and Seitz, Steven M and Miller, Daniel and Brossard, Evan}, +booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, +pages={4873--4882}, +year={2016} +}",
+MORPH non-commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+MORPH commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+FaceScrub,A data-driven approach to cleaning large face datasets,Proc. IEEE International Conference on Image Processing (ICIP),2014,,,H.-W. Ng,S. Winkler,,,,,,,,,
+MIFS,Spoofing Faces Using Makeup: An Investigative Study,"Proc. of 3rd IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), (New Delhi, India)",2017,,,C. Chen,A. Dantcheva,T. Swearingen,A. Ross,,,,,http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf,,
+MIW,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+VMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+YMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,1,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+YMU,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,2,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+MsCeleb,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,European Conference on Computer Vision,2016,,,"Guo, Yandong","Zhang, Lei","Hu, Yuxiao","He, Xiaodong","Gao, Jianfeng",,,,https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/MSCeleb-1M-a.pdf,"@INPROCEEDINGS { guo2016msceleb, + author = {Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng}, + title = {M{S}-{C}eleb-1{M}: A Dataset and Benchmark for Large Scale Face Recognition}, + booktitle = {European Conference on Computer Vision}, + year = {2016}, + organization={Springer}}",
+PIPA,Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues,arXiv:1501.05703 [cs.CV],2015,,,Ning Zhang, Manohar Paluri,Yaniv Taigman,Rob Fergus,Lubomir Bourdev,,,,https://arxiv.org/pdf/1501.05703.pdf,"@inproceedings{piper, + Author = {Ning Zhang and Manohar Paluri and Yaniv Taigman and Rob Fergus and Lubomir Bourdev}, + Title = {Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues}, + Eprint = {arXiv:1501.05703}, + Year = {2015}}",
+PubFig,Attribute and Simile Classifiers for Face Verification,International Conference on Computer Vision (ICCV),2009,,,Neeraj Kumar,Alexander C. Berg,Peter N. Belhumeur,Shree K. Nayar,,,,,http://www.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ICCV09.pdf,,
+SCUT-FBP,SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception,arXiv:1511.02459 [cs.CV],2015,,,Duorui Xie,Lingyu Liang,Lianwen Jin,Jie Xu,Mengru Li,,,,https://arxiv.org/ftp/arxiv/papers/1511/1511.02459.pdf,,
+RaFD ,Presentation and validation of the Radboud Faces Database,Cognition & Emotion,2010,1377-1388,24.8,"Langner, O.","Dotsch, R."," Bijlstra, G.","Wigboldus, D.H.J.","Hawk, S.T.","van Knippenberg, A.",,,http://dx.doi.org/10.1080/02699930903485076,DOI: 10.1080/02699930903485076,
+MUCT,The MUCT Landmarked Face Database,Pattern Recognition Association of South Africa,2010,,,,S. Milborrow,J. Morkel,F. Nicolls,,,,,http://www.milbo.org/muct/The-MUCT-Landmarked-Face-Database.pdf,"@article{Milborrow10, + author={S. Milborrow and J. Morkel and F. Nicolls}, + title={{The MUCT Landmarked Face Database}}, + journal={Pattern Recognition Association of South Africa}, + year=2010, + note={\url{http://www.milbo.org/muct}} +}",
+IJB-A,"Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A +",Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2015,1931-1939,07-12-June-2015,"Klare, B. F.","Klein, B.","Taborsky, E.","Blanton, A.","Cheney, J.","Allen, K., ... Jain, A. K.",,,http://ieeexplore.ieee.org/document/7298803/,"DOI: 10.1109/CVPR.2015.7298803 @inbook{882e95bdca414797b4a8e2bfcb5b1fa4, +title = ""Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A"", +abstract = ""Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark."", +author = ""Klare, {Brendan F.} and Ben Klein and Emma Taborsky and Austin Blanton and Jordan Cheney and Kristen Allen and Patrick Grother and Alan Mah and Mark Burge and Jain, {Anil K.}"", +year = ""2015"", +month = ""10"", +doi = ""10.1109/CVPR.2015.7298803"", +isbn = ""9781467369640"", +volume = ""07-12-June-2015"", +pages = ""1931--1939"", +booktitle = ""Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"", +publisher = ""IEEE Computer Society"", + +} +",
+FaceScrub,A data-driven approach to cleaning large face datasets,"Proc. IEEE International Conference on Image Processing (ICIP), Paris, France",2014,,,H.-W. Ng,S. Winkler,,,,,,,http://vintage.winklerbros.net/Publications/icip2014a.pdf,,
+Face Research Lab London Set,Face Research Lab London Set. figshare,,2017,,,"DeBruine, Lisa","Jones, Benedict",,,,,,,https://doi.org/10.6084/m9.figshare.5047666.v3,,
+CK,Comprehensive Database for Facial Expression Analysis,"Proceedings of the Fourth IEEE International Conferenc +e on Automatic Face and Gesture Recognition +(FG'00) +",2000,484-490,,"Kanade, T.","Cohn, J. F.","Tian, Y.",,,,,,http://www.pitt.edu/~jeffcohn/biblio/Cohn-Kanade_Database.pdf,,
+Columbia Gaze Data Set,Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction,ACM Symposium on User Interface Software and Technology (UIST),2013,271-280,,B.A. Smith,Q. Yin,S.K. Feiner,S.K. Nayar,,,,,http://www.cs.columbia.edu/~brian/publications/gaze_locking.html,,
+UCF Selfie,"How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia",,2015,,,Mahdi M. Kalayeh,Misrak Seifu,Wesna LaLanne,Mubarak Shah,,,,,,,
+UCF101,THUMOS Challenge: Action Recognition with a Large Number of Classes,,2015,,,"Gorban, A.","Idrees, H.","Jiang, Y.-G.","Roshan Zamir, A.","Laptev, I.","Shah, M. and Sukthankar, R.",,1,http://www.thumos.info/,"@misc{THUMOS15, + author = ""Gorban, A. and Idrees, H. and Jiang, Y.-G. and Roshan Zamir, A. and Laptev, + I. and Shah, M. and Sukthankar, R."", + title = ""{THUMOS} Challenge: Action Recognition with a Large + Number of Classes"", + howpublished = ""\url{http://www.thumos.info/}"", + Year = {2015}}",
+UCF101,UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,CRCV-TR-12-01,2012,,,"Soomro, K.","Roshan Zamir, A.","Shah, M.",,,,,2,,"@inproceedings{UCF101, + author = {Soomro, K. and Roshan Zamir, A. and Shah, M.}, + booktitle = {CRCV-TR-12-01}, + title = {{UCF101}: A Dataset of 101 Human Actions Classes From + Videos in The Wild}, + year = {2012}}",
+SVW,Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis,"Proc. International Conference on Automatic Face and Gesture Recognition (FG 2015), Ljubljana, Slovenia",2015,,,Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus,"John Wood,",Dean Craven,,,http://cvlab.cse.msu.edu/pdfs/Morteza_FG2015.pdf," @inproceedings{ sports-videos-in-the-wild-svw-a-video-dataset-for-sports-analysis, + author = { Seyed Morteza Safdarnejad and Xiaoming Liu and Lalita Udpa and Brooks Andrus and John Wood and Dean Craven }, + title = { Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis }, + booktitle = { Proc. International Conference on Automatic Face and Gesture Recognition }, + address = { Ljubljana, Slovenia }, + month = { May }, + year = { 2015 }, +} ",
+CK+,The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression,"Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA",2010,94-101,,"Ambadar, Z.","Cohn, J.F.","Kanade, T.","Lucey, P.","Matthews, I.A.","Saragih, J.M.",,,http://ieeexplore.ieee.org/document/5543262/,"@article{Lucey2010TheEC, + title={The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression}, + author={Patrick Lucey and Jeffrey F. Cohn and Takeo Kanade and Jason M. Saragih and Zara Ambadar and Iain A. Matthews}, + journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops}, + year={2010}, + pages={94-101} +}",
+Names and Faces,Names and Faces ,U.C. Berkeley Technical Report,Jan. 2007,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,1,http://www.cs.berkeley.edu/%7Eaberg/papers/journal_berg.pdf,,
+Names and Faces,Who's in the Picture ,NIPS,2004,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,David A. Forsyth,,,,2,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_whos_in_the_picture.pdf,,
+Names and Faces,Names and Faces in the News,"Computer Vision and Pattern Recognition (CVPR), Washington D.C.",2004,848-854,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,3,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_names_and_faces.pdf,,
+YaleFaces,Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,"IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition",1997,711--720,17(7),P. N. Bellhumer,J. Hespanha,D. Kriegman,,,,,,,,
+Yale Face Database B,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,PAMI,2001,,,Athinodoros Georghiades,Peter Belhumeur,David Kriegman,,,,,,,,
+PIE,"The CMU Pose, Illumination, and Expression Database",IEEE Transactions on Pattern Analysis and Machine Intelligence,Dec 2003,"25, No. 12",,T. Sim,S. Baker,M. Bsat,,,,,,http://www.cs.cmu.edu/~simonb/pie_db/pami.pdf,,
+IMDB,Deep expectation of real and apparent age from a single image without facial landmarks,International Journal of Computer Vision (IJCV),Jul 2016,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,1,,"@article{Rothe-IJCV-2016, + author = {Rasmus Rothe and Radu Timofte and Luc Van Gool}, + title = {Deep expectation of real and apparent age from a single image without facial landmarks}, + journal = {International Journal of Computer Vision (IJCV)}, + year = {2016}, + month = {July}, +}",
+IMDB,DEX: Deep EXpectation of apparent age from a single image,IEEE International Conference on Computer Vision Workshops (ICCVW),Dec 2015,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,2,,"@InProceedings{Rothe-ICCVW-2015, + author = {Rasmus Rothe and Radu Timofte and Luc Van Gool}, + title = {DEX: Deep EXpectation of apparent age from a single image}, + booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)}, + year = {2015}, + month = {December}, +}",
\ No newline at end of file diff --git a/datasets/citations-3.csv b/datasets/citations-3.csv new file mode 100644 index 00000000..57db254d --- /dev/null +++ b/datasets/citations-3.csv @@ -0,0 +1,51 @@ +Database Name,Title,Journal/Pub/Conference,Year,Pages,Volume,Author1,Author2,Author3,Author4,Author5,Author 6,PDF,Priority,URL,bibtex_reference_only,notes
+imSitu,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,"(1) Computer Science & Engineering, University of Washington, Seattle, WA +(2) Allen Institute for Artificial Intelligence (AI2), Seattle, WA",,,,Mark Yatskar,Luke Zettlemoyer,Ali Farhadi,,,,,,https://homes.cs.washington.edu/~my89/publications/situations.pdf,,
+JAFFE,Coding Facial Expressions with Gabor Wavelets,3rd IEEE International Conference on Automatic Face and Gesture Recognition,1998,200-205,,Michael J. Lyons,Shigeru Akemastu,Miyuki Kamachi,Jiro Gyoba,,,,,http://www.kasrl.org/fg98-1.pdf,,
+UCF101,THUMOS Challenge: Action Recognition with a Large Number of Classes,,2015,,,"Gorban, A.","Idrees, H.","Jiang, Y.-G.","Roshan Zamir, A.","Laptev, I.","Shah, M. and Sukthankar, R.",,1,http://www.thumos.info/,"@misc{THUMOS15, + author = ""Gorban, A. and Idrees, H. and Jiang, Y.-G. and Roshan Zamir, A. and Laptev, + I. and Shah, M. and Sukthankar, R."", + title = ""{THUMOS} Challenge: Action Recognition with a Large + Number of Classes"", + howpublished = ""\url{http://www.thumos.info/}"", + Year = {2015}}",
+IMFDB,Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,"National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)",2013,,,Shankar Setty,et al,,,,,,,http://cvit.iiit.ac.in/projects/IMFDB/imfdb.pdf,"@InProceedings{imfdb, +author = {Shankar Setty, Moula Husain, Parisa Beham, Jyothi Gudavalli, Menaka Kandasamy, Radhesyam Vaddi, Vidyagouri Hemadri, J C Karure, Raja Raju, Rajan, Vijay Kumar and C V Jawahar}, +title = {{I}ndian {M}ovie {F}ace {D}atabase: {A} {B}enchmark for {F}ace {R}ecognition {U}nder {W}ide {V}ariations}, +booktitle = {National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)}, +month = {Dec}, +year = {2013} +} ",
+LFW-a,Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics,"IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 33(10),",2011,,,Lior Wolf,Tal Hassner,Yaniv Taigman,,,,,,http://www.openu.ac.il/home/hassner/projects/Patchlbp/WolfHassnerTaigman_TPAMI11.pdf,,
+MORPH commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+SCUT-FBP,SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception,arXiv:1511.02459 [cs.CV],2015,,,Duorui Xie,Lingyu Liang,Lianwen Jin,Jie Xu,Mengru Li,,,,https://arxiv.org/ftp/arxiv/papers/1511/1511.02459.pdf,,
+Names and Faces,Names and Faces in the News,"Computer Vision and Pattern Recognition (CVPR), Washington D.C.",2004,848-854,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,3,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_names_and_faces.pdf,,
+FaceScrub,A data-driven approach to cleaning large face datasets,Proc. IEEE International Conference on Image Processing (ICIP),2014,,,H.-W. Ng,S. Winkler,,,,,,,,,
+UCF Selfie,"How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia",,2015,,,Mahdi M. Kalayeh,Misrak Seifu,Wesna LaLanne,Mubarak Shah,,,,,,,
+300-W,A semi-automatic methodology for facial landmark annotation,"Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2013). Oregon, USA,",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,3,,,
+YMU,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,2,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+WIDER,Recognize Complex Events from Static Images by Fusing Deep Channels,2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2015,,,"Xiong, Yuanjun and Zhu, Kai and Lin, Dahua and Tang, Xiaoou",,,,,,,,,,
+YaleFaces,Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,"IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition",1997,711--720,17(7),P. N. Bellhumer,J. Hespanha,D. Kriegman,,,,,,,,
+PubFig,Attribute and Simile Classifiers for Face Verification,International Conference on Computer Vision (ICCV),2009,,,Neeraj Kumar,Alexander C. Berg,Peter N. Belhumeur,Shree K. Nayar,,,,,http://www.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ICCV09.pdf,,
+Face Research Lab London Set,Face Research Lab London Set. figshare,,2017,,,"DeBruine, Lisa","Jones, Benedict",,,,,,,https://doi.org/10.6084/m9.figshare.5047666.v3,,
+MALF,Fine-grained Evaluation on Face Detection in the Wild.,Proceedings of the 11th IEEE International Conference on Automatic Face and Gesture Recognition Conference and Workshops.,2015,,,Bin Yang*,Junjie Yan*,Zhen Lei,Stan Z. Li,,,,,http://www.cbsr.ia.ac.cn/faceevaluation/faceevaluation15.pdf,"@inproceedings{faceevaluation15, +title={Fine-grained Evaluation on Face Detection in the Wild}, +author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z}, +booktitle={Automatic Face and Gesture Recognition (FG), 11th IEEE International +Conference on}, +year={2015}, +organization={IEEE} +}",
+FaceScrub,A data-driven approach to cleaning large face datasets,"Proc. IEEE International Conference on Image Processing (ICIP), Paris, France",2014,,,H.-W. Ng,S. Winkler,,,,,,,http://vintage.winklerbros.net/Publications/icip2014a.pdf,,
+VMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+300-W,300 faces In-the-wild challenge: Database and results,"Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation ""In-The-Wild""",2016,,,C. Sagonas,E. Antonakos,"G, Tzimiropoulos",S. Zafeiriou,M. Pantic,,,1,,,
+CK,Comprehensive Database for Facial Expression Analysis,"Proceedings of the Fourth IEEE International Conferenc +e on Automatic Face and Gesture Recognition +(FG'00) +",2000,484-490,,"Kanade, T.","Cohn, J. F.","Tian, Y.",,,,,,http://www.pitt.edu/~jeffcohn/biblio/Cohn-Kanade_Database.pdf,,
+MegaFace 2,Level Playing Field for Million Scale Face Recognition,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017,,,"Nech, Aaron","Kemelmacher-Shlizerman, Ira",,,,,,If you're participating or using data from Challenge 2 please cite:,https://homes.cs.washington.edu/~kemelmi/ms.pdf,"@inproceedings{nech2017level, +title={Level Playing Field For Million Scale Face Recognition}, +author={Nech, Aaron and Kemelmacher-Shlizerman, Ira}, +booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, +year={2017} +}",
diff --git a/datasets/citations.csv b/datasets/citations.csv new file mode 120000 index 00000000..a2ab42cc --- /dev/null +++ b/datasets/citations.csv @@ -0,0 +1 @@ +citations-2018310.csv
\ No newline at end of file diff --git a/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv new file mode 100644 index 00000000..38f502f9 --- /dev/null +++ b/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv @@ -0,0 +1 @@ +300 faces in-the-wild challenge: The first facial landmark localization challenge|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|2013|396|15|7861246476672124064|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|http://scholar.google.com/scholar?cites=7861246476672124064&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7861246476672124064&hl=en&as_sdt=0,5|None|Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well as the lack of a evaluation protocol makes it difficult to compare performance between different systems. In this paper, we present the … diff --git a/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv new file mode 100644 index 00000000..eaaf1a93 --- /dev/null +++ b/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv @@ -0,0 +1 @@ +300 faces in-the-wild challenge: Database and results|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0262885616000147|2016|141|9|4741451765657920988|None|http://scholar.google.com/scholar?cites=4741451765657920988&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4741451765657920988&hl=en&as_sdt=0,5|None|Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues.(a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low.(b) Most … diff --git a/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv b/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv new file mode 100644 index 00000000..c1bf1f38 --- /dev/null +++ b/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv @@ -0,0 +1 @@ +A data-driven approach to cleaning large face datasets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7025068/|2014|163|8|9390951279725836807|None|http://scholar.google.com/scholar?cites=9390951279725836807&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9390951279725836807&hl=en&as_sdt=0,5|None|Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the … diff --git a/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv b/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv new file mode 100644 index 00000000..31bf7b39 --- /dev/null +++ b/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv @@ -0,0 +1 @@ +A semi-automatic methodology for facial landmark annotation|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|2013|225|16|15744661091744891|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|http://scholar.google.com/scholar?cites=15744661091744891&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15744661091744891&hl=en&as_sdt=0,5|None|Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the … diff --git a/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv b/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv new file mode 100644 index 00000000..035e5e0f --- /dev/null +++ b/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv @@ -0,0 +1 @@ +Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6130513/|2011|402|12|2106290919498044015|None|http://scholar.google.com/scholar?cites=2106290919498044015&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=2106290919498044015&hl=en&as_sdt=0,5|None|Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations … diff --git a/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv b/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv new file mode 100644 index 00000000..1d6e856b --- /dev/null +++ b/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv @@ -0,0 +1,2 @@ +Attribute and simile classifiers for face verification|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5459250/|2009|1231|22|4063408445858122425|None|http://scholar.google.com/scholar?cites=4063408445858122425&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4063408445858122425&hl=en&as_sdt=0,5|None|We present two novel methods for face verification. Our first method-“attribute” classifiers-uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (eg, gender, race, and age). Our second method-“simile” classifiers-removes the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact … +Attribute and simile classifiers for face verification|None|2009|10|0|7848300437118808957|None|http://scholar.google.com/scholar?cites=7848300437118808957&as_sdt=2005&sciodt=0,5&hl=en|None|None|None diff --git a/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv b/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv new file mode 100644 index 00000000..074471b7 --- /dev/null +++ b/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv @@ -0,0 +1 @@ +Automatic facial makeup detection with application in face recognition|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6612994/|2013|83|9|6724137544116293607|None|http://scholar.google.com/scholar?cites=6724137544116293607&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6724137544116293607&hl=en&as_sdt=0,5|None|Facial makeup has the ability to alter the appearance of a person. Such an alteration can degrade the accuracy of automated face recognition systems, as well as that of meth-ods estimating age and beauty from faces. In this work, we design a method to automatically detect the presence of makeup in face images. The proposed algorithm extracts a feature vector that captures the shape, texture and color characteristics of the input face, and employs a classifier to determine the presence or absence of makeup. Besides extracting … diff --git a/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv b/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv new file mode 100644 index 00000000..0b36206c --- /dev/null +++ b/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv @@ -0,0 +1 @@ +Beyond frontal faces: Improving person recognition using multiple cues|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Zhang_Beyond_Frontal_Faces_2015_CVPR_paper.html|2015|70|13|4032136205953773331|None|http://scholar.google.com/scholar?cites=4032136205953773331&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4032136205953773331&hl=en&as_sdt=0,5|None|We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of~ 2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition … diff --git a/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv b/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv new file mode 100644 index 00000000..86c81060 --- /dev/null +++ b/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv @@ -0,0 +1 @@ +Can facial cosmetics affect the matching accuracy of face recognition systems?|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6374605/|2012|90|12|13294356886705558975|None|http://scholar.google.com/scholar?cites=13294356886705558975&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13294356886705558975&hl=en&as_sdt=0,5|None|The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying … diff --git a/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv b/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv new file mode 100644 index 00000000..36b9e0cf --- /dev/null +++ b/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv @@ -0,0 +1 @@ +Coding facial expressions with gabor wavelets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/670949/|1998|1623|14|1158641084116311050|None|http://scholar.google.com/scholar?cites=1158641084116311050&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1158641084116311050&hl=en&as_sdt=0,5|None|A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input … diff --git a/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv b/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv new file mode 100644 index 00000000..e97d2c56 --- /dev/null +++ b/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv @@ -0,0 +1 @@ +Comprehensive database for facial expression analysis|http://scholar.google.com/https://www.computer.org/csdl/proceedings/fg/2000/0580/00/05800046.pdf|2000|2538|29|17655514864522744044|http://scholar.google.com/https://www.computer.org/csdl/proceedings/fg/2000/0580/00/05800046.pdf|http://scholar.google.com/scholar?cites=17655514864522744044&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17655514864522744044&hl=en&as_sdt=0,5|None|Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and … diff --git a/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv new file mode 100644 index 00000000..b3728548 --- /dev/null +++ b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv @@ -0,0 +1 @@ +Dex: Deep expectation of apparent age from a single image|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.html|2015|155|15|12384435539194835187|None|http://scholar.google.com/scholar?cites=12384435539194835187&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12384435539194835187&hl=en&as_sdt=0,5|None|In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the … diff --git a/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv b/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv new file mode 100644 index 00000000..ed47fdef --- /dev/null +++ b/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv @@ -0,0 +1 @@ +Deep expectation of real and apparent age from a single image without facial landmarks|http://scholar.google.com/https://link.springer.com/article/10.1007/s11263-016-0940-3|2018|135|7|11164967779616636427|None|http://scholar.google.com/scholar?cites=11164967779616636427&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11164967779616636427&hl=en&as_sdt=0,5|None|In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for … diff --git a/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv new file mode 100644 index 00000000..5cd26552 --- /dev/null +++ b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv @@ -0,0 +1 @@ +Distance estimation of an unknown person from a portrait|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-10590-1_21|2014|7|9|11199246855168438175|None|http://scholar.google.com/scholar?cites=11199246855168438175&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11199246855168438175&hl=en&as_sdt=0,5|None|We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each … diff --git a/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv b/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv new file mode 100644 index 00000000..252d269c --- /dev/null +++ b/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv @@ -0,0 +1,2 @@ +Eigenfaces vs. fisherfaces: Recognition using class specific linear projection|http://www.dtic.mil/docs/citations/AD1015508|1997|13228|67|13084856655998519010|None|http://scholar.google.com/scholar?cites=13084856655998519010&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13084856655998519010&hl=en&as_sdt=0,5|None|We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image spaceif the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do … +Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection|http://scholar.google.com/https://link.springer.com/chapter/10.1007/BFb0015522|1996|609|8|10500235270745853797|None|http://scholar.google.com/scholar?cites=10500235270745853797&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10500235270745853797&hl=en&as_sdt=0,5|None|We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination direction lie in a 3-D linear subspace of the high dimensional feature space—if the face is a Lambertian surface without self-shadowing. However, since faces are not truly Lambertian surfaces and do … diff --git a/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv b/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv new file mode 100644 index 00000000..eeb0adb1 --- /dev/null +++ b/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv @@ -0,0 +1 @@ +Fddb: A benchmark for face detection in unconstrained settings|http://www.cs.umass.edu/~elm/papers/fddb.pdf|2010|525|13|17267836250801810690|http://www.cs.umass.edu/~elm/papers/fddb.pdf|http://scholar.google.com/scholar?cites=17267836250801810690&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17267836250801810690&hl=en&as_sdt=0,5|None|Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two … diff --git a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv new file mode 100644 index 00000000..2f1e41af --- /dev/null +++ b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv @@ -0,0 +1,2 @@ +Face recognition in unconstrained videos with matched background similarity|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5995566/|2011|657|10|5401801956686441353|None|http://scholar.google.com/scholar?cites=5401801956686441353&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5401801956686441353&hl=en&as_sdt=0,5|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions.(a) We present a comprehensive … +Face Recognition in Unconstrained Videos with Matched Background Similarity|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|2012|0|0|None|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|None|None|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this work we make the following contributions:(a) We present a comprehensive … diff --git a/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv b/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv new file mode 100644 index 00000000..de202138 --- /dev/null +++ b/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv @@ -0,0 +1 @@ +Face swapping: automatically replacing faces in photographs|http://scholar.google.com/https://dl.acm.org/citation.cfm?id=1360638|2008|228|9|8277329192835426026|None|http://scholar.google.com/scholar?cites=8277329192835426026&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8277329192835426026&hl=en&as_sdt=0,5|None|In this paper, we present a complete system for automatic face replacement in images. Our system uses a large library of face images created automatically by downloading images from the internet, extracting faces using face detection software, and aligning each extracted face to a common coordinate system. This library is constructed off-line, once, and can be efficiently accessed during face replacement. Our replacement algorithm has three main stages. First, given an input image, we detect all faces that are present, align them to the … diff --git a/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv b/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv new file mode 100644 index 00000000..43da8a92 --- /dev/null +++ b/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv @@ -0,0 +1 @@ +Face detection, pose estimation, and landmark localization in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6248014/|2012|1693|7|4876235110904982186|None|http://scholar.google.com/scholar?cites=4876235110904982186&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4876235110904982186&hl=en&as_sdt=0,5|None|We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a … diff --git a/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv b/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv new file mode 100644 index 00000000..a03e78e4 --- /dev/null +++ b/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv @@ -0,0 +1 @@ +Facetracer: A search engine for large collections of images with faces|http://scholar.google.com/https://link.springer.com/10.1007/978-3-540-88693-8_25|2008|324|15|10337130688446550899|None|http://scholar.google.com/scholar?cites=10337130688446550899&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10337130688446550899&hl=en&as_sdt=0,5|None|We have created the first image search engine based entirely on faces. Using simple text queries such as “smiling men with blond hair and mustaches,” users can search through over 3.1 million faces which have been automatically labeled on the basis of several facial attributes. Faces in our database have been extracted and aligned from images downloaded from the internet using a commercial face detector, and the number of images and attributes continues to grow daily. Our classification approach uses a novel combination of Support … diff --git a/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv b/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv new file mode 100644 index 00000000..249cea3a --- /dev/null +++ b/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv @@ -0,0 +1 @@ +Fine-grained evaluation on face detection in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7163158/|2015|24|7|6318135921321197431|None|http://scholar.google.com/scholar?cites=6318135921321197431&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6318135921321197431&hl=en&as_sdt=0,5|None|Current evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date. We propose a new face detection dataset MALF (short for Multi-Attribute Labelled Faces), which contains 5,250 images collected from the Internet and~ 12,000 labelled faces. The MALF dataset highlights in two main features: 1) It is the largest dataset for evaluation of face detection in the wild, and the annotation of multiple facial attributes makes it possible for fine-grained performance analysis. 2) To … diff --git a/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv b/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv new file mode 100644 index 00000000..e22f032b --- /dev/null +++ b/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv @@ -0,0 +1 @@ +From facial parts responses to face detection: A deep learning approach|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Yang_From_Facial_Parts_ICCV_2015_paper.html|2015|213|12|1818335115841631894|None|http://scholar.google.com/scholar?cites=1818335115841631894&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1818335115841631894&hl=en&as_sdt=0,5|None|In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging … diff --git a/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv b/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv new file mode 100644 index 00000000..b9feb021 --- /dev/null +++ b/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv @@ -0,0 +1 @@ +Indian movie face database: a benchmark for face recognition under wide variations|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6776225/|2013|29|7|10194316221634175118|None|http://scholar.google.com/scholar?cites=10194316221634175118&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10194316221634175118&hl=en&as_sdt=0,5|None|Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various … diff --git a/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv b/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv new file mode 100644 index 00000000..a47bb51d --- /dev/null +++ b/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv @@ -0,0 +1 @@ +Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6712710/|2013|10|4|3607022377504716214|None|http://scholar.google.com/scholar?cites=3607022377504716214&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3607022377504716214&hl=en&as_sdt=0,5|None|In this work we investigate a truly novel and extremely unique biometric problem: face-based recognition for transgender persons. A transgender person is someone who under goes a gender transformation via hormone replacement therapy; that is, a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen. Transgender hormone replacement therapy causes physical changes in the body and face. This work provides a preliminary investigation into the effects of these changes on face … diff --git a/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv b/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv new file mode 100644 index 00000000..23c90284 --- /dev/null +++ b/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv @@ -0,0 +1 @@ +Labeled faces in the wild: A database forstudying face recognition in unconstrained environments|http://scholar.google.com/https://hal.inria.fr/inria-00321923/|2008|3014|37|6713997626354918066|None|http://scholar.google.com/scholar?cites=6713997626354918066&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6713997626354918066&hl=en&as_sdt=0,5|None|Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is … diff --git a/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv new file mode 100644 index 00000000..9cd388eb --- /dev/null +++ b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv @@ -0,0 +1 @@ +Large age-gap face verification by feature injection in deep networks|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0167865517300727|2017|12|8|6980699793307007950|None|http://scholar.google.com/scholar?cites=6980699793307007950&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6980699793307007950&hl=en&as_sdt=0,5|None|This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection … diff --git a/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv b/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv new file mode 100644 index 00000000..f7130a67 --- /dev/null +++ b/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv @@ -0,0 +1 @@ +Level playing field for million scale face recognition|http://openaccess.thecvf.com/content_cvpr_2017/papers/Nech_Level_Playing_Field_CVPR_2017_paper.pdf|2017|35|11|12932836311624990730|http://openaccess.thecvf.com/content_cvpr_2017/papers/Nech_Level_Playing_Field_CVPR_2017_paper.pdf|http://scholar.google.com/scholar?cites=12932836311624990730&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12932836311624990730&hl=en&as_sdt=0,5|None|Face recognition has the perception of a solved problem, however when tested at the million-scale exhibits dramatic variation in accuracies across the different algorithms [11]. Are the algorithms very different? Is access to good/big training data their secret weapon? Where should face recognition improve? To address those questions, we created a benchmark, MF2, that requires all algorithms to be trained on same data, and tested at the million scale. MF2 is a public large-scale set with 672K identities and 4.7 M photos created with the goal to … diff --git a/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv new file mode 100644 index 00000000..0fa7a800 --- /dev/null +++ b/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv @@ -0,0 +1 @@ +Localizing parts of faces using a consensus of exemplars|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6412675/|2013|740|13|8801930631236620204|None|http://scholar.google.com/scholar?cites=8801930631236620204&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8801930631236620204&hl=en&as_sdt=0,5|None|We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and … diff --git a/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv b/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv new file mode 100644 index 00000000..a41ffc41 --- /dev/null +++ b/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv @@ -0,0 +1 @@ +Morph: A longitudinal image database of normal adult age-progression|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1613043/|2006|673|6|4438087728034206462|None|http://scholar.google.com/scholar?cites=4438087728034206462&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4438087728034206462&hl=en&as_sdt=0,5|None|This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, eg face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus … diff --git a/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv b/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv new file mode 100644 index 00000000..3af655d0 --- /dev/null +++ b/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv @@ -0,0 +1 @@ +Ms-celeb-1m: A dataset and benchmark for large-scale face recognition|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-46487-9_6|2016|189|6|7096719334274798105|None|http://scholar.google.com/scholar?cites=7096719334274798105&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7096719334274798105&hl=en&as_sdt=0,5|None|In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world … diff --git a/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv b/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv new file mode 100644 index 00000000..89746fe9 --- /dev/null +++ b/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv @@ -0,0 +1 @@ +Presentation and validation of the Radboud Faces Database|http://scholar.google.com/https://www.tandfonline.com/doi/abs/10.1080/02699930903485076|2010|1052|10|9283473996856444608|None|http://scholar.google.com/scholar?cites=9283473996856444608&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9283473996856444608&hl=en&as_sdt=0,5|None|Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several … diff --git a/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv b/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv new file mode 100644 index 00000000..c5540c9a --- /dev/null +++ b/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv @@ -0,0 +1 @@ +Pruning training sets for learning of object categories|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1467308/|2005|98|19|6629732990128685315|None|http://scholar.google.com/scholar?cites=6629732990128685315&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6629732990128685315&hl=en&as_sdt=0,5|None|Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning … diff --git a/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv b/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv new file mode 100644 index 00000000..d632534a --- /dev/null +++ b/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv @@ -0,0 +1 @@ +Recognize complex events from static images by fusing deep channels|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_054.pdf|2015|62|18|14301800130435949971|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_054.pdf|http://scholar.google.com/scholar?cites=14301800130435949971&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14301800130435949971&hl=en&as_sdt=0,5|None|A considerable portion of web images capture events that occur in our personal lives or social activities. In this paper, we aim to develop an effective method for recognizing events from such images. Despite the sheer amount of study on event recognition, most existing methods rely on videos and are not directly applicable to this task. Generally, events are complex phenomena that involve interactions among people and objects, and therefore analysis of event photos requires techniques that can go beyond recognizing individual … diff --git a/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv new file mode 100644 index 00000000..c1245878 --- /dev/null +++ b/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv @@ -0,0 +1 @@ +Robust face landmark estimation under occlusion|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2013/html/Burgos-Artizzu_Robust_Face_Landmark_2013_ICCV_paper.html|2013|441|16|6035683787196907858|None|http://scholar.google.com/scholar?cites=6035683787196907858&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6035683787196907858&hl=en&as_sdt=0,5|None|Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (eg food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of handling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using … diff --git a/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv b/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv new file mode 100644 index 00000000..9215c287 --- /dev/null +++ b/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv @@ -0,0 +1 @@ +SCUT-FBP: A benchmark dataset for facial beauty perception|http://scholar.google.com/https://arxiv.org/abs/1511.02459|2015|17|4|3066282784180910292|None|http://scholar.google.com/scholar?cites=3066282784180910292&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3066282784180910292&hl=en&as_sdt=0,5|None|In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self … diff --git a/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv new file mode 100644 index 00000000..503356df --- /dev/null +++ b/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv @@ -0,0 +1 @@ +Situation recognition: Visual semantic role labeling for image understanding|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html|2016|53|9|14769542088507071062|None|http://scholar.google.com/scholar?cites=14769542088507071062&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14769542088507071062&hl=en&as_sdt=0,5|None|This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including:(1) the main activity (eg, clipping),(2) the participating actors, objects, substances, and locations (eg, man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (eg, the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a … diff --git a/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv b/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv new file mode 100644 index 00000000..6fa46797 --- /dev/null +++ b/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv @@ -0,0 +1 @@ +Spoofing faces using makeup: An investigative study|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7947686/|2017|6|8|1291042674502639294|None|http://scholar.google.com/scholar?cites=1291042674502639294&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1291042674502639294&hl=en&as_sdt=0,5|None|Makeup can be used to alter the facial appearance of a person. Previous studies have established the potential of using makeup to obfuscate the identity of an individual with respect to an automated face matcher. In this work, we analyze the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another person's facial appearance. In this regard, we first assemble a set of face images downloaded from the internet where individuals use facial cosmetics to impersonate … diff --git a/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv b/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv new file mode 100644 index 00000000..a9d02e41 --- /dev/null +++ b/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv @@ -0,0 +1 @@ +Sports videos in the wild (SVW): A video dataset for sports analysis|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7163105/|2015|14|11|10001086963759053928|None|http://scholar.google.com/scholar?cites=10001086963759053928&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10001086963759053928&hl=en&as_sdt=0,5|None|Considering the enormous creation rate of usergenerated videos on websites like YouTube, there is an immediate need for automatic categorization, recognition and analysis of videos. To develop algorithms for analyzing user-generated videos, unconstrained and representative datasets are of great significance. For this purpose, we collected a dataset of Sports Videos in the Wild (SVW), consisting of videos captured by users of the leading sports training mobile app (Coach's Eye) while practicing a sport or watching a game. The dataset … diff --git a/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv b/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv new file mode 100644 index 00000000..d494c943 --- /dev/null +++ b/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv @@ -0,0 +1 @@ +The Do's and Don'ts for CNN-based Face Verification.|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|2017|21|7|16583671830808674747|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|http://scholar.google.com/scholar?cites=16583671830808674747&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=16583671830808674747&hl=en&as_sdt=0,5|None|While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered. In this paper, we explore the following questions that are critical to face recognition research:(i) Can we train on still images and expect the systems to work on videos?(ii) Are deeper datasets better than wider datasets?(iii) Does adding label noise lead to improvement in performance of deep networks?(iv) Is alignment needed for face … diff --git a/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv b/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv new file mode 100644 index 00000000..399b667f --- /dev/null +++ b/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv @@ -0,0 +1 @@ +The extended cohn-kanade dataset (ck+) a complete expression dataset for action unit and emotion-speified expression|None|2010|3|0|13817454793240235261|None|http://scholar.google.com/scholar?cites=13817454793240235261&as_sdt=2005&sciodt=0,5&hl=en|None|None|None diff --git a/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv b/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv new file mode 100644 index 00000000..68dc8389 --- /dev/null +++ b/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv @@ -0,0 +1 @@ +The megaface benchmark: 1 million faces for recognition at scale|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.html|2016|159|11|6051410257476935491|None|http://scholar.google.com/scholar?cites=6051410257476935491&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6051410257476935491&hl=en&as_sdt=0,5|None|Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms … diff --git a/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv b/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv new file mode 100644 index 00000000..9cbf069c --- /dev/null +++ b/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv @@ -0,0 +1 @@ +UCF101: A dataset of 101 human actions classes from videos in the wild|http://scholar.google.com/https://arxiv.org/abs/1212.0402|2012|1211|11|10653986877352008041|None|http://scholar.google.com/scholar?cites=10653986877352008041&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10653986877352008041&hl=en&as_sdt=0,5|None|We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large … diff --git a/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv b/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv new file mode 100644 index 00000000..adf44ba7 --- /dev/null +++ b/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv @@ -0,0 +1 @@ +Umdfaces: An annotated face dataset for training deep networks|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/8272731/|2017|35|5|15417824747310072694|None|http://scholar.google.com/scholar?cites=15417824747310072694&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15417824747310072694&hl=en&as_sdt=0,5|None|Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. In this paper, we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. 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\ No newline at end of file diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 00000000..c94fbb15 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,13 @@ +{ + "name": "megapixels_dev", + "version": "1.0.0", + "lockfileVersion": 1, + "requires": true, + "dependencies": { + "node-fetch": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.2.0.tgz", + "integrity": "sha512-OayFWziIxiHY8bCUyLX6sTpDH8Jsbp4FfYd1j1f7vZyfgkcOnAyM4oQR16f8a0s7Gl/viMGRey8eScYk4V4EZA==" + } + } +} diff --git a/package.json b/package.json new file mode 100644 index 00000000..377ae72d --- /dev/null +++ b/package.json @@ -0,0 +1,22 @@ +{ + "name": "megapixels_dev", + "version": "1.0.0", + "description": "", + "main": "index.js", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "repository": { + "type": "git", + "url": "git+https://github.com/adamhrv/megapixels_dev.git" + }, + "author": "", + "license": "UNLICENSED", + "bugs": { + "url": "https://github.com/adamhrv/megapixels_dev/issues" + }, + "homepage": "https://github.com/adamhrv/megapixels_dev#readme", + "dependencies": { + "node-fetch": "^2.2.0" + } +} diff --git a/s2-dump-ids.py b/s2-dump-ids.py new file mode 100644 index 00000000..66ff6d77 --- /dev/null +++ b/s2-dump-ids.py @@ -0,0 +1,30 @@ +import os +import gzip +import glob +import json +import click +from util import * + +PAPER_JSON_DIR = 'datasets/s2/papers' + +@click.command() +def s2_dump_ids(): + ids = {} + for fn in glob.iglob('{}/**/*.json'.format(PAPER_JSON_DIR), recursive=True): + process_paper(fn, ids) + id_list = list(ids.keys()) + print("Wrote {} ids".format(len(id_list))) + write_json('ids.json', id_list) + +def process_paper(fn, ids): + with open(fn, 'r') as f: + data = json.load(f) + ids[data['paperId']] = True + for cite in data['citations']: + ids[cite['paperId']] = True + +def paper_path(paper_id): + return '{}/{}/{}'.format(DATA_DIR, paper_id[0:3], paper_id) + +if __name__ == '__main__': + s2_dump_ids() diff --git a/s2-extract-papers.py b/s2-extract-papers.py new file mode 100644 index 00000000..90323e6e --- /dev/null +++ b/s2-extract-papers.py @@ -0,0 +1,50 @@ +import os +import gzip +import glob +import click + +S2_DIR = '/media/blue/undisclosed/semantic-scholar/corpus-2018-05-03' +DATA_DIR = '/home/lens/undisclosed/megapixels_dev/datasets/s2/db_papers' + +@click.command() +@click.option('--input', '-i', default='ids.json', help='List of IDs to extract from the big dataset.') +def fetch_entries(fn): + ids = load_id_lookup(fn) + for filename in glob.iglob('{}/*.gz'.format(S2_DIR)): + search_dataset_shard('{}/{}'.format(S2_DIR, filename), ids) + +def search_dataset_shard(fn, ids): + with gzip.open(fn, 'r') as f: + for line in f.readlines(): + process_paper(str(line)[2:-3]) + +def process_paper(line): + paper_id = line.split('"id":"', 2)[1].split('"', 2)[0] + if paper_id in ids: + print(paper_id) + del ids[paper_id] + write_paper(paper_id, line) + +def load_id_lookup(fn): + lookup = {} + ids = read_json(fn) + for paper_id in ids: + path = paper_path(paper_id) + if not os.path.exists(path): + lookup[paper_id] = True + return lookup + +def paper_path(paper_id): + return '{}/{}/{}'.format(DATA_DIR, paper_id[0:3], paper_id) + +def write_paper(paper_id, data): + dir = paper_path(paper_id) + fn = dir + '/paper.json' + if os.path.exists(fn): + return + os.makedirs(dir, exist_ok=True) + with open(fn, 'wb') as f: + f.write(data) + +if __name__ == '__main__': + fetch_entries() diff --git a/s2-papers.py b/s2-papers.py new file mode 100644 index 00000000..d5e8bcd4 --- /dev/null +++ b/s2-papers.py @@ -0,0 +1,64 @@ +import os +import sys +import csv +import subprocess +import time +import random +import re +import json +import click +from s2 import SemanticScholarAPI +from util import * + +''' +s2 search API format: +results +matchedAuthors +matchedPresentations +query +querySuggestions +results +stats +totalPages +totalResults +''' + +s2 = SemanticScholarAPI() + +@click.command() +@click.option('--index', '-n', default=0, help='Index of CSV.') +@click.option('--depth', '-d', default=1, help='Depth to recurse.') +def fetch_papers(index, depth): + keys, lines = read_citation_list(index) + for line in lines: + label = line[0] + title = re.sub(r'[^-0-9a-zA-Z ]+', '', line[1]) + entry_fn = './datasets/s2/entries/{}.json'.format(title) + if not os.path.exists(entry_fn): + print('not found: {}'.format(entry_fn)) + continue + result = read_json(entry_fn) + paper_id = result['id'] + paper = fetch_paper(paper_id) + # get all of the paper's citations + +def fetch_paper(paper_id): + os.makedirs('./datasets/s2/papers/{}/{}'.format(paper_id[0:2], paper_id), exist_ok=True) + paper_fn = './datasets/s2/papers/{}/{}/paper.json'.format(paper_id[0:2], paper_id) + if os.path.exists(paper_fn): + return read_json(paper_fn) + print(paper_id) + paper = s2.paper(paper_id) + if paper is None: + print("Got none paper??") + time.sleep(random.randint(20, 30)) + paper = s2.paper(paper_id) + if paper is None: + print("Paper not found") + return None + write_json(paper_fn, paper) + time.sleep(random.randint(5, 10)) + return paper + +if __name__ == '__main__': + fetch_papers() diff --git a/s2-search.py b/s2-search.py new file mode 100644 index 00000000..b9e8db2d --- /dev/null +++ b/s2-search.py @@ -0,0 +1,46 @@ +import os +import sys +import csv +import subprocess +import time +import random +import re +import simplejson as json +import click +from s2 import SemanticScholarAPI +from util import * + +''' +s2 search API format: +results +matchedAuthors +matchedPresentations +query +querySuggestions +results +stats +totalPages +totalResults +''' + +@click.command() +@click.option('--index', '-n', default=0, help='Index of CSV.') +def fetch_entries(index): + keys, lines = read_citation_list(index) + s2 = SemanticScholarAPI() + for line in lines: + label = line[0] + title = re.sub(r'[^-0-9a-zA-Z ]+', '', line[1]) + entry_fn = './datasets/s2/entries/{}.json'.format(title) + if not os.path.exists(entry_fn): + results = s2.search(title) + write_json(dump_fn, results) + if len(results['results']) == 0: + print("No results for {}".format(title)) + else: + print(title) + write_json(entry_fn, results['results'][0]) + time.sleep(random.randint(10, 20)) + +if __name__ == '__main__': + fetch_entries() @@ -0,0 +1,151 @@ +import requests + +class AuthorStub(object): + + def __init__(self, **kwargs): + self.authorId = kwargs["authorId"] + self.name = kwargs.get("name", None) + self.url = kwargs.get("url", None) + + def __str__(self): + return self.authorId + + def __eq__(self, other): + return isinstance(other, AuthorStub) and self.authorId == other.authorId + + def __hash__(self): + return hash(self.authorId) + + def json(self): + return { + "authorId" : self.authorId, + "name" : self.name, + "url" : self.url + } + + def full(self, **kwargs): + return SemanticScholarAPI.author(self.authorId, **kwargs) + +class Author(object): + + def __init__(self, **kwargs): + self._kwargs = kwargs + self.authorId = kwargs["authorId"] + self.name = kwargs.get("name", None) + self.aliases = kwargs.get("aliases", []) + self.citationVelocity = kwargs.get("citationVelocity", None) + self.influentialCitationCount = kwargs.get("influentialCitationCount", None) + self.url = kwargs.get("url", None) + + def __str__(self): + return self.authorId + + def __eq__(self, other): + return isinstance(other, Author) and self.authorId == other.authorId + + def __hash__(self): + return hash(self.authorId) + + def papers(self): + for elem in self._kwargs.get("papers", []): + yield SemanticScholarAPI.paper(elem["paperId"]) + + def json(self): + return self._kwargs + +class PaperStub(object): + + def __init__(self, **kwargs): + self.paperId = kwargs["paperId"] + self.isInfluential = kwargs.get("isInfluential", False) + self.title = kwargs.get("title", None) + self.venue = kwargs.get("venue", None) + self.year = kwargs.get("year", None) + + def __str__(self): + return self.paperId + + def __eq__(self, other): + return isinstance(other, PaperStub) and self.paperId == other.paperId + + def __hash__(self): + return hash(self.paperId) + + def json(self): + return { + "paperId" : self.paperId, + "isInfluential" : self.isInfluential, + "title" : self.title, + "venue" : self.venue, + "year" : self.year, + } + + def full(self, **kwargs): + return SemanticScholarAPI.paper(self.paperId, **kwargs) + +class Paper(object): + + def __init__(self, **kwargs): + self.doi = kwargs.get("doi", None) + self.citationVelocity = kwargs.get("citationVelocity", None) + self.influentialCitationCount = kwargs.get("influentialCitationCount", None) + self.url = kwargs.get("url", None) + self.authors = [AuthorStub(**elem) for elem in kwargs.get("authors", [])] + self.citations = [PaperStub(**elem) for elem in kwargs.get("citations", [])] + self.references = [PaperStub(**elem) for elem in kwargs.get("references", [])] + self.venue = kwargs.get("venue", None) + self.references = kwargs.get("references", []) + self.title = kwargs.get("title", None) + self.year = kwargs.get("year", None) + + def __str__(self): + return self.paperId + + def __eq__(self, other): + return isinstance(other, Paper) and self.paperId == other.paperId + + def __hash__(self): + return hash(self.paperId) + + def json(self): + return self._kwargs + +class SemanticScholarAPI(object): + BASE_URL = "http://api.semanticscholar.org/v1" + AUTHOR_ENDPOINT = "{}/{}".format(BASE_URL, "author") + PAPER_ENDPOINT = "{}/{}".format(BASE_URL, "paper") + SEARCH_ENDPOINT = "https://www.semanticscholar.org/api/1/search" + + @staticmethod + def paper(paper_id, **kwargs): + url = "{}/{}".format(SemanticScholarAPI.PAPER_ENDPOINT, paper_id) + resp = requests.get(url, params=kwargs) + return None if resp.status_code != 200 else resp.json() # Paper(**resp.json()) + + @staticmethod + def author(author_id, **kwargs): + url = "{}/{}".format(SemanticScholarAPI.AUTHOR_ENDPOINT, author_id) + resp = requests.get(url, params=kwargs) + return None if resp.status_code != 200 else resp.json() # Author(**resp.json()) + + @staticmethod + def pdf_url(paper_id): + return "http://pdfs.semanticscholar.org/{}/{}.pdf".format(paper_id[:4], paper_id[4:]) + + @staticmethod + def search(q): + resp = requests.post(SemanticScholarAPI.SEARCH_ENDPOINT, json={ + 'authors': [], + 'coAuthors': [], + 'facets': {}, + 'page': 1, + 'pageSize': 10, + 'publicationTypes': [], + 'queryString': q, + 'requireViewablePdf': False, + 'sort': "relevance", + 'venues': [], + 'yearFilter': None, + }) + # print(resp.status_code) + return None if resp.status_code != 200 else resp.json() diff --git a/scholar-fetch.py b/scholar-fetch.py new file mode 100644 index 00000000..e206b058 --- /dev/null +++ b/scholar-fetch.py @@ -0,0 +1,44 @@ +import os +import sys +import csv +import subprocess +import time +import random +import re +import click + +@click.command() +@click.option('--index', '-n', default=1, help='Index of CSV.') +def fetch_entries(index): + keys, lines = read_citation_list(index) + + for line in lines: + label = line[0] + title = line[1] + entries_fn = './datasets/scholar/entries/{}.csv'.format(title) + # print(entries_fn) + if not os.path.exists(entries_fn): + with open(entries_fn, 'w') as f: + t = re.sub(r'[^-0-9a-zA-Z ]+', '', title) + print(t) + subprocess.call([ + './vendor/scholar.py', + '-t', '-A', t, '--csv', + ], stdout=f) + time.sleep(random.randint(30, 60)) + + +def read_citation_list(index): + filename = './datasets/citations.csv' + # fn, ext = os.path.splitext(filename) + # in_fn = fn + '-' + str(index) + ext + # with open(in_fn, 'r') as f: + with open(filename, 'r') as f: + reader = csv.reader(f) + lines = list(reader) + keys = lines[0] + lines = lines[1:] + return keys, lines + +if __name__ == '__main__': + fetch_entries() diff --git a/split-csv.py b/split-csv.py new file mode 100644 index 00000000..62dc1597 --- /dev/null +++ b/split-csv.py @@ -0,0 +1,46 @@ +import os +import sys +import csv +from math import ceil +import subprocess +import random + +import click + +@click.command() +@click.option('--count', '-c', default=2, help='Number of subdivisions.') +@click.option('--shuffle/--no_shuffle', default=False, help='Whether to shuffle.') +@click.argument('filename') +def split_csv(count, shuffle, filename): + """Split a CSV into groups.""" + with open(filename, 'r') as f: + reader = csv.reader(f) + lines = list(reader) + keys = lines[0] + lines = lines[1:] + + fn, ext = os.path.splitext(filename) + if shuffle: + random.shuffle(lines) + for index, chunk in enumerate(chunks(lines, count)): + out_fn = fn + '-' + str(index+1) + ext + write_csv(out_fn, keys, chunk) + # sys.exit(1) + +# Write a CSV +def write_csv(fn, keys, chunk): + print(fn) + with open(fn, 'w') as f: + writer = csv.writer(f) + if keys is not None: + writer.writerow(keys) + for row in chunk: + writer.writerow(row) + +# Split an array into chunks +def chunks(l, n): + n = max(1, ceil(len(l) / n)) + return (l[i:i+n] for i in range(0, len(l), n)) + +if __name__ == '__main__': + split_csv() diff --git a/util.py b/util.py new file mode 100644 index 00000000..dae3f67c --- /dev/null +++ b/util.py @@ -0,0 +1,31 @@ +import os +import csv +import simplejson as json + +def read_citation_list(index=0): + filename = './datasets/citations.csv' + if index > 0: + fn, ext = os.path.splitext(filename) + filename = fn + '-' + str(index) + ext + with open(filename, 'r') as f: + reader = csv.reader(f) + lines = list(reader) + keys = lines[0] + lines = lines[1:] + return keys, lines + +def read_json(fn): + with open(fn, 'r') as json_file: + return json.load(json_file) + +def write_json(fn, data): + with open(fn, 'w') as outfile: + json.dump(data, outfile) + +def write_csv(fn, keys, rows): + with open(fn, 'w') as f: + writer = csv.writer(f) + if keys is not None: + writer.writerow(keys) + for row in rows: + writer.writerow(row) diff --git a/vendor/scholar.py b/vendor/scholar.py new file mode 100755 index 00000000..13ccd439 --- /dev/null +++ b/vendor/scholar.py @@ -0,0 +1,1310 @@ +#! /usr/bin/env python +""" +This module provides classes for querying Google Scholar and parsing +returned results. It currently *only* processes the first results +page. It is not a recursive crawler. +""" +# ChangeLog +# --------- +# +# 2.11 The Scholar site seems to have become more picky about the +# number of results requested. The default of 20 in scholar.py +# could cause HTTP 503 responses. scholar.py now doesn't request +# a maximum unless you provide it at the comment line. (For the +# time being, you still cannot request more than 20 results.) +# +# 2.10 Merged a fix for the "TypError: quote_from_bytes()" problem on +# Python 3.x from hinnefe2. +# +# 2.9 Fixed Unicode problem in certain queries. Thanks to smidm for +# this contribution. +# +# 2.8 Improved quotation-mark handling for multi-word phrases in +# queries. Also, log URLs %-decoded in debugging output, for +# easier interpretation. +# +# 2.7 Ability to extract content excerpts as reported in search results. +# Also a fix to -s|--some and -n|--none: these did not yet support +# passing lists of phrases. This now works correctly if you provide +# separate phrases via commas. +# +# 2.6 Ability to disable inclusion of patents and citations. This +# has the same effect as unchecking the two patents/citations +# checkboxes in the Scholar UI, which are checked by default. +# Accordingly, the command-line options are --no-patents and +# --no-citations. +# +# 2.5: Ability to parse global result attributes. This right now means +# only the total number of results as reported by Scholar at the +# top of the results pages (e.g. "About 31 results"). Such +# global result attributes end up in the new attrs member of the +# used ScholarQuery class. To render those attributes, you need +# to use the new --txt-globals flag. +# +# Rendering global results is currently not supported for CSV +# (as they don't fit the one-line-per-article pattern). For +# grepping, you can separate the global results from the +# per-article ones by looking for a line prefix of "[G]": +# +# $ scholar.py --txt-globals -a "Einstein" +# [G] Results 11900 +# +# Title Can quantum-mechanical description of physical reality be considered complete? +# URL http://journals.aps.org/pr/abstract/10.1103/PhysRev.47.777 +# Year 1935 +# Citations 12804 +# Versions 80 +# Cluster ID 8174092782678430881 +# Citations list http://scholar.google.com/scholar?cites=8174092782678430881&as_sdt=2005&sciodt=0,5&hl=en +# Versions list http://scholar.google.com/scholar?cluster=8174092782678430881&hl=en&as_sdt=0,5 +# +# 2.4: Bugfixes: +# +# - Correctly handle Unicode characters when reporting results +# in text format. +# +# - Correctly parse citation-only (i.e. linkless) results in +# Google Scholar results. +# +# 2.3: Additional features: +# +# - Direct extraction of first PDF version of an article +# +# - Ability to pull up an article cluster's results directly. +# +# This is based on work from @aliparsai on GitHub -- thanks! +# +# - Suppress missing search results (so far shown as "None" in +# the textual output form. +# +# 2.2: Added a logging option that reports full HTML contents, for +# debugging, as well as incrementally more detailed logging via +# -d up to -dddd. +# +# 2.1: Additional features: +# +# - Improved cookie support: the new --cookie-file options +# allows the reuse of a cookie across invocations of the tool; +# this allows higher query rates than would otherwise result +# when invoking scholar.py repeatedly. +# +# - Workaround: remove the num= URL-encoded argument from parsed +# URLs. For some reason, Google Scholar decides to propagate +# the value from the original query into the URLs embedded in +# the results. +# +# 2.0: Thorough overhaul of design, with substantial improvements: +# +# - Full support for advanced search arguments provided by +# Google Scholar +# +# - Support for retrieval of external citation formats, such as +# BibTeX or EndNote +# +# - Simple logging framework to track activity during execution +# +# 1.7: Python 3 and BeautifulSoup 4 compatibility, as well as printing +# of usage info when no options are given. Thanks to Pablo +# Oliveira (https://github.com/pablooliveira)! +# +# Also a bunch of pylinting and code cleanups. +# +# 1.6: Cookie support, from Matej Smid (https://github.com/palmstrom). +# +# 1.5: A few changes: +# +# - Tweak suggested by Tobias Isenberg: use unicode during CSV +# formatting. +# +# - The option -c|--count now understands numbers up to 100 as +# well. Likewise suggested by Tobias. +# +# - By default, text rendering mode is now active. This avoids +# confusion when playing with the script, as it used to report +# nothing when the user didn't select an explicit output mode. +# +# 1.4: Updates to reflect changes in Scholar's page rendering, +# contributed by Amanda Hay at Tufts -- thanks! +# +# 1.3: Updates to reflect changes in Scholar's page rendering. +# +# 1.2: Minor tweaks, mostly thanks to helpful feedback from Dan Bolser. +# Thanks Dan! +# +# 1.1: Made author field explicit, added --author option. +# +# Don't complain about missing docstrings: pylint: disable-msg=C0111 +# +# Copyright 2010--2017 Christian Kreibich. All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are +# met: +# +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above +# copyright notice, this list of conditions and the following +# disclaimer in the documentation and/or other materials provided +# with the distribution. +# +# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED +# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, +# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING +# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +# POSSIBILITY OF SUCH DAMAGE. + +import optparse +import os +import re +import sys +import warnings + +try: + # Try importing for Python 3 + # pylint: disable-msg=F0401 + # pylint: disable-msg=E0611 + from urllib.request import HTTPCookieProcessor, Request, build_opener + from urllib.parse import quote, unquote + from http.cookiejar import MozillaCookieJar +except ImportError: + # Fallback for Python 2 + from urllib2 import Request, build_opener, HTTPCookieProcessor + from urllib import quote, unquote + from cookielib import MozillaCookieJar + +# Import BeautifulSoup -- try 4 first, fall back to older +try: + from bs4 import BeautifulSoup +except ImportError: + try: + from BeautifulSoup import BeautifulSoup + except ImportError: + print('We need BeautifulSoup, sorry...') + sys.exit(1) + +# Support unicode in both Python 2 and 3. In Python 3, unicode is str. +if sys.version_info[0] == 3: + unicode = str # pylint: disable-msg=W0622 + encode = lambda s: unicode(s) # pylint: disable-msg=C0103 +else: + def encode(s): + if isinstance(s, basestring): + return s.encode('utf-8') # pylint: disable-msg=C0103 + else: + return str(s) + + +class Error(Exception): + """Base class for any Scholar error.""" + + +class FormatError(Error): + """A query argument or setting was formatted incorrectly.""" + + +class QueryArgumentError(Error): + """A query did not have a suitable set of arguments.""" + + +class SoupKitchen(object): + """Factory for creating BeautifulSoup instances.""" + + @staticmethod + def make_soup(markup, parser=None): + """Factory method returning a BeautifulSoup instance. The created + instance will use a parser of the given name, if supported by + the underlying BeautifulSoup instance. + """ + if 'bs4' in sys.modules: + # We support parser specification. If the caller didn't + # specify one, leave it to BeautifulSoup to pick the most + # suitable one, but suppress the user warning that asks to + # select the most suitable parser ... which BS then + # selects anyway. + if parser is None: + warnings.filterwarnings('ignore', 'No parser was explicitly specified') + return BeautifulSoup(markup, parser) + + return BeautifulSoup(markup) + +class ScholarConf(object): + """Helper class for global settings.""" + + VERSION = '2.10' + LOG_LEVEL = 1 + MAX_PAGE_RESULTS = 10 # Current default for per-page results + SCHOLAR_SITE = 'http://scholar.google.com' + + # USER_AGENT = 'Mozilla/5.0 (X11; U; FreeBSD i386; en-US; rv:1.9.2.9) Gecko/20100913 Firefox/3.6.9' + # Let's update at this point (3/14): + USER_AGENT = 'Mozilla/5.0 (X11; Linux x86_64; rv:27.0) Gecko/20100101 Firefox/27.0' + + # If set, we will use this file to read/save cookies to enable + # cookie use across sessions. + COOKIE_JAR_FILE = None + +class ScholarUtils(object): + """A wrapper for various utensils that come in handy.""" + + LOG_LEVELS = {'error': 1, + 'warn': 2, + 'info': 3, + 'debug': 4} + + @staticmethod + def ensure_int(arg, msg=None): + try: + return int(arg) + except ValueError: + raise FormatError(msg) + + @staticmethod + def log(level, msg): + if level not in ScholarUtils.LOG_LEVELS.keys(): + return + if ScholarUtils.LOG_LEVELS[level] > ScholarConf.LOG_LEVEL: + return + sys.stderr.write('[%5s] %s' % (level.upper(), msg + '\n')) + sys.stderr.flush() + + +class ScholarArticle(object): + """ + A class representing articles listed on Google Scholar. The class + provides basic dictionary-like behavior. + """ + def __init__(self): + # The triplets for each keyword correspond to (1) the actual + # value, (2) a user-suitable label for the item, and (3) an + # ordering index: + self.attrs = { + 'title': [None, 'Title', 0], + 'url': [None, 'URL', 1], + 'year': [None, 'Year', 2], + 'num_citations': [0, 'Citations', 3], + 'num_versions': [0, 'Versions', 4], + 'cluster_id': [None, 'Cluster ID', 5], + 'url_pdf': [None, 'PDF link', 6], + 'url_citations': [None, 'Citations list', 7], + 'url_versions': [None, 'Versions list', 8], + 'url_citation': [None, 'Citation link', 9], + 'excerpt': [None, 'Excerpt', 10], + } + + # The citation data in one of the standard export formats, + # e.g. BibTeX. + self.citation_data = None + + def __getitem__(self, key): + if key in self.attrs: + return self.attrs[key][0] + return None + + def __len__(self): + return len(self.attrs) + + def __setitem__(self, key, item): + if key in self.attrs: + self.attrs[key][0] = item + else: + self.attrs[key] = [item, key, len(self.attrs)] + + def __delitem__(self, key): + if key in self.attrs: + del self.attrs[key] + + def set_citation_data(self, citation_data): + self.citation_data = citation_data + + def as_txt(self): + # Get items sorted in specified order: + items = sorted(list(self.attrs.values()), key=lambda item: item[2]) + # Find largest label length: + max_label_len = max([len(str(item[1])) for item in items]) + fmt = '%%%ds %%s' % max_label_len + res = [] + for item in items: + if item[0] is not None: + res.append(fmt % (item[1], item[0])) + return '\n'.join(res) + + def as_csv(self, header=False, sep='|'): + # Get keys sorted in specified order: + keys = [pair[0] for pair in \ + sorted([(key, val[2]) for key, val in list(self.attrs.items())], + key=lambda pair: pair[1])] + res = [] + if header: + res.append(sep.join(keys)) + res.append(sep.join([unicode(self.attrs[key][0]) for key in keys])) + return '\n'.join(res) + + def as_citation(self): + """ + Reports the article in a standard citation format. This works only + if you have configured the querier to retrieve a particular + citation export format. (See ScholarSettings.) + """ + return self.citation_data or '' + + +class ScholarArticleParser(object): + """ + ScholarArticleParser can parse HTML document strings obtained from + Google Scholar. This is a base class; concrete implementations + adapting to tweaks made by Google over time follow below. + """ + def __init__(self, site=None): + self.soup = None + self.article = None + self.site = site or ScholarConf.SCHOLAR_SITE + self.year_re = re.compile(r'\b(?:20|19)\d{2}\b') + + def handle_article(self, art): + """ + The parser invokes this callback on each article parsed + successfully. In this base class, the callback does nothing. + """ + + def handle_num_results(self, num_results): + """ + The parser invokes this callback if it determines the overall + number of results, as reported on the parsed results page. The + base class implementation does nothing. + """ + + def parse(self, html): + """ + This method initiates parsing of HTML content, cleans resulting + content as needed, and notifies the parser instance of + resulting instances via the handle_article callback. + """ + self.soup = SoupKitchen.make_soup(html) + + # This parses any global, non-itemized attributes from the page. + self._parse_globals() + + # Now parse out listed articles: + for div in self.soup.findAll(ScholarArticleParser._tag_results_checker): + self._parse_article(div) + self._clean_article() + if self.article['title']: + self.handle_article(self.article) + + def _clean_article(self): + """ + This gets invoked after we have parsed an article, to do any + needed cleanup/polishing before we hand off the resulting + article. + """ + if self.article['title']: + self.article['title'] = self.article['title'].strip() + + def _parse_globals(self): + tag = self.soup.find(name='div', attrs={'id': 'gs_ab_md'}) + if tag is not None: + raw_text = tag.findAll(text=True) + # raw text is a list because the body contains <b> etc + if raw_text is not None and len(raw_text) > 0: + try: + num_results = raw_text[0].split()[1] + # num_results may now contain commas to separate + # thousands, strip: + num_results = num_results.replace(',', '') + num_results = int(num_results) + self.handle_num_results(num_results) + except (IndexError, ValueError): + pass + + def _parse_article(self, div): + self.article = ScholarArticle() + + for tag in div: + if not hasattr(tag, 'name'): + continue + + if tag.name == 'div' and self._tag_has_class(tag, 'gs_rt') and \ + tag.h3 and tag.h3.a: + self.article['title'] = ''.join(tag.h3.a.findAll(text=True)) + self.article['url'] = self._path2url(tag.h3.a['href']) + if self.article['url'].endswith('.pdf'): + self.article['url_pdf'] = self.article['url'] + + if tag.name == 'font': + for tag2 in tag: + if not hasattr(tag2, 'name'): + continue + if tag2.name == 'span' and \ + self._tag_has_class(tag2, 'gs_fl'): + self._parse_links(tag2) + + def _parse_links(self, span): + for tag in span: + if not hasattr(tag, 'name'): + continue + if tag.name != 'a' or tag.get('href') is None: + continue + + if tag.get('href').startswith('/scholar?cites'): + if hasattr(tag, 'string') and tag.string.startswith('Cited by'): + self.article['num_citations'] = \ + self._as_int(tag.string.split()[-1]) + + # Weird Google Scholar behavior here: if the original + # search query came with a number-of-results limit, + # then this limit gets propagated to the URLs embedded + # in the results page as well. Same applies to + # versions URL in next if-block. + self.article['url_citations'] = \ + self._strip_url_arg('num', self._path2url(tag.get('href'))) + + # We can also extract the cluster ID from the versions + # URL. Note that we know that the string contains "?", + # from the above if-statement. + args = self.article['url_citations'].split('?', 1)[1] + for arg in args.split('&'): + if arg.startswith('cites='): + self.article['cluster_id'] = arg[6:] + + if tag.get('href').startswith('/scholar?cluster'): + if hasattr(tag, 'string') and tag.string.startswith('All '): + self.article['num_versions'] = \ + self._as_int(tag.string.split()[1]) + self.article['url_versions'] = \ + self._strip_url_arg('num', self._path2url(tag.get('href'))) + + if tag.getText().startswith('Import'): + self.article['url_citation'] = self._path2url(tag.get('href')) + + + @staticmethod + def _tag_has_class(tag, klass): + """ + This predicate function checks whether a BeatifulSoup Tag instance + has a class attribute. + """ + res = tag.get('class') or [] + if type(res) != list: + # BeautifulSoup 3 can return e.g. 'gs_md_wp gs_ttss', + # so split -- conveniently produces a list in any case + res = res.split() + return klass in res + + @staticmethod + def _tag_results_checker(tag): + return tag.name == 'div' \ + and ScholarArticleParser._tag_has_class(tag, 'gs_r') + + @staticmethod + def _as_int(obj): + try: + return int(obj) + except ValueError: + return None + + def _path2url(self, path): + """Helper, returns full URL in case path isn't one.""" + if path.startswith('http://'): + return path + if not path.startswith('/'): + path = '/' + path + return self.site + path + + def _strip_url_arg(self, arg, url): + """Helper, removes a URL-encoded argument, if present.""" + parts = url.split('?', 1) + if len(parts) != 2: + return url + res = [] + for part in parts[1].split('&'): + if not part.startswith(arg + '='): + res.append(part) + return parts[0] + '?' + '&'.join(res) + + +class ScholarArticleParser120201(ScholarArticleParser): + """ + This class reflects update to the Scholar results page layout that + Google recently. + """ + def _parse_article(self, div): + self.article = ScholarArticle() + + for tag in div: + if not hasattr(tag, 'name'): + continue + + if tag.name == 'h3' and self._tag_has_class(tag, 'gs_rt') and tag.a: + self.article['title'] = ''.join(tag.a.findAll(text=True)) + self.article['url'] = self._path2url(tag.a['href']) + if self.article['url'].endswith('.pdf'): + self.article['url_pdf'] = self.article['url'] + + if tag.name == 'div' and self._tag_has_class(tag, 'gs_a'): + year = self.year_re.findall(tag.text) + self.article['year'] = year[0] if len(year) > 0 else None + + if tag.name == 'div' and self._tag_has_class(tag, 'gs_fl'): + self._parse_links(tag) + + +class ScholarArticleParser120726(ScholarArticleParser): + """ + This class reflects update to the Scholar results page layout that + Google made 07/26/12. + """ + def _parse_article(self, div): + self.article = ScholarArticle() + + for tag in div: + if not hasattr(tag, 'name'): + continue + if str(tag).lower().find('.pdf'): + if tag.find('div', {'class': 'gs_ttss'}): + self._parse_links(tag.find('div', {'class': 'gs_ttss'})) + + if tag.name == 'div' and self._tag_has_class(tag, 'gs_ri'): + # There are (at least) two formats here. In the first + # one, we have a link, e.g.: + # + # <h3 class="gs_rt"> + # <a href="http://dl.acm.org/citation.cfm?id=972384" class="yC0"> + # <b>Honeycomb</b>: creating intrusion detection signatures using + # honeypots + # </a> + # </h3> + # + # In the other, there's no actual link -- it's what + # Scholar renders as "CITATION" in the HTML: + # + # <h3 class="gs_rt"> + # <span class="gs_ctu"> + # <span class="gs_ct1">[CITATION]</span> + # <span class="gs_ct2">[C]</span> + # </span> + # <b>Honeycomb</b> automated ids signature creation using honeypots + # </h3> + # + # We now distinguish the two. + try: + atag = tag.h3.a + self.article['title'] = ''.join(atag.findAll(text=True)) + self.article['url'] = self._path2url(atag['href']) + if self.article['url'].endswith('.pdf'): + self.article['url_pdf'] = self.article['url'] + except: + # Remove a few spans that have unneeded content (e.g. [CITATION]) + for span in tag.h3.findAll(name='span'): + span.clear() + self.article['title'] = ''.join(tag.h3.findAll(text=True)) + + if tag.find('div', {'class': 'gs_a'}): + year = self.year_re.findall(tag.find('div', {'class': 'gs_a'}).text) + self.article['year'] = year[0] if len(year) > 0 else None + + if tag.find('div', {'class': 'gs_fl'}): + self._parse_links(tag.find('div', {'class': 'gs_fl'})) + + if tag.find('div', {'class': 'gs_rs'}): + # These are the content excerpts rendered into the results. + raw_text = tag.find('div', {'class': 'gs_rs'}).findAll(text=True) + if len(raw_text) > 0: + raw_text = ''.join(raw_text) + raw_text = raw_text.replace('\n', '') + self.article['excerpt'] = raw_text + + +class ScholarQuery(object): + """ + The base class for any kind of results query we send to Scholar. + """ + def __init__(self): + self.url = None + + # The number of results requested from Scholar -- not the + # total number of results it reports (the latter gets stored + # in attrs, see below). + self.num_results = None + + # Queries may have global result attributes, similar to + # per-article attributes in ScholarArticle. The exact set of + # attributes may differ by query type, but they all share the + # basic data structure: + self.attrs = {} + + def set_num_page_results(self, num_page_results): + self.num_results = ScholarUtils.ensure_int( + num_page_results, + 'maximum number of results on page must be numeric') + + def get_url(self): + """ + Returns a complete, submittable URL string for this particular + query instance. The URL and its arguments will vary depending + on the query. + """ + return None + + def _add_attribute_type(self, key, label, default_value=None): + """ + Adds a new type of attribute to the list of attributes + understood by this query. Meant to be used by the constructors + in derived classes. + """ + if len(self.attrs) == 0: + self.attrs[key] = [default_value, label, 0] + return + idx = max([item[2] for item in self.attrs.values()]) + 1 + self.attrs[key] = [default_value, label, idx] + + def __getitem__(self, key): + """Getter for attribute value. Returns None if no such key.""" + if key in self.attrs: + return self.attrs[key][0] + return None + + def __setitem__(self, key, item): + """Setter for attribute value. Does nothing if no such key.""" + if key in self.attrs: + self.attrs[key][0] = item + + def _parenthesize_phrases(self, query): + """ + Turns a query string containing comma-separated phrases into a + space-separated list of tokens, quoted if containing + whitespace. For example, input + + 'some words, foo, bar' + + becomes + + '"some words" foo bar' + + This comes in handy during the composition of certain queries. + """ + if query.find(',') < 0: + return query + phrases = [] + for phrase in query.split(','): + phrase = phrase.strip() + if phrase.find(' ') > 0: + phrase = '"' + phrase + '"' + phrases.append(phrase) + return ' '.join(phrases) + + +class ClusterScholarQuery(ScholarQuery): + """ + This version just pulls up an article cluster whose ID we already + know about. + """ + SCHOLAR_CLUSTER_URL = ScholarConf.SCHOLAR_SITE + '/scholar?' \ + + 'cluster=%(cluster)s' \ + + '%(num)s' + + def __init__(self, cluster=None): + ScholarQuery.__init__(self) + self._add_attribute_type('num_results', 'Results', 0) + self.cluster = None + self.set_cluster(cluster) + + def set_cluster(self, cluster): + """ + Sets search to a Google Scholar results cluster ID. + """ + msg = 'cluster ID must be numeric' + self.cluster = ScholarUtils.ensure_int(cluster, msg) + + def get_url(self): + if self.cluster is None: + raise QueryArgumentError('cluster query needs cluster ID') + + urlargs = {'cluster': self.cluster } + + for key, val in urlargs.items(): + urlargs[key] = quote(encode(val)) + + # The following URL arguments must not be quoted, or the + # server will not recognize them: + urlargs['num'] = ('&num=%d' % self.num_results + if self.num_results is not None else '') + + return self.SCHOLAR_CLUSTER_URL % urlargs + + +class SearchScholarQuery(ScholarQuery): + """ + This version represents the search query parameters the user can + configure on the Scholar website, in the advanced search options. + """ + SCHOLAR_QUERY_URL = ScholarConf.SCHOLAR_SITE + '/scholar?' \ + + 'as_q=%(words)s' \ + + '&as_epq=%(phrase)s' \ + + '&as_oq=%(words_some)s' \ + + '&as_eq=%(words_none)s' \ + + '&as_occt=%(scope)s' \ + + '&as_sauthors=%(authors)s' \ + + '&as_publication=%(pub)s' \ + + '&as_ylo=%(ylo)s' \ + + '&as_yhi=%(yhi)s' \ + + '&as_vis=%(citations)s' \ + + '&btnG=&hl=en' \ + + '%(num)s' \ + + '&as_sdt=%(patents)s%%2C5' + + def __init__(self): + ScholarQuery.__init__(self) + self._add_attribute_type('num_results', 'Results', 0) + self.words = None # The default search behavior + self.words_some = None # At least one of those words + self.words_none = None # None of these words + self.phrase = None + self.scope_title = False # If True, search in title only + self.author = None + self.pub = None + self.timeframe = [None, None] + self.include_patents = True + self.include_citations = True + + def set_words(self, words): + """Sets words that *all* must be found in the result.""" + self.words = words + + def set_words_some(self, words): + """Sets words of which *at least one* must be found in result.""" + self.words_some = words + + def set_words_none(self, words): + """Sets words of which *none* must be found in the result.""" + self.words_none = words + + def set_phrase(self, phrase): + """Sets phrase that must be found in the result exactly.""" + self.phrase = phrase + + def set_scope(self, title_only): + """ + Sets Boolean indicating whether to search entire article or title + only. + """ + self.scope_title = title_only + + def set_author(self, author): + """Sets names that must be on the result's author list.""" + self.author = author + + def set_pub(self, pub): + """Sets the publication in which the result must be found.""" + self.pub = pub + + def set_timeframe(self, start=None, end=None): + """ + Sets timeframe (in years as integer) in which result must have + appeared. It's fine to specify just start or end, or both. + """ + if start: + start = ScholarUtils.ensure_int(start) + if end: + end = ScholarUtils.ensure_int(end) + self.timeframe = [start, end] + + def set_include_citations(self, yesorno): + self.include_citations = yesorno + + def set_include_patents(self, yesorno): + self.include_patents = yesorno + + def get_url(self): + if self.words is None and self.words_some is None \ + and self.words_none is None and self.phrase is None \ + and self.author is None and self.pub is None \ + and self.timeframe[0] is None and self.timeframe[1] is None: + raise QueryArgumentError('search query needs more parameters') + + # If we have some-words or none-words lists, we need to + # process them so GS understands them. For simple + # space-separeted word lists, there's nothing to do. For lists + # of phrases we have to ensure quotations around the phrases, + # separating them by whitespace. + words_some = None + words_none = None + + if self.words_some: + words_some = self._parenthesize_phrases(self.words_some) + if self.words_none: + words_none = self._parenthesize_phrases(self.words_none) + + urlargs = {'words': self.words or '', + 'words_some': words_some or '', + 'words_none': words_none or '', + 'phrase': self.phrase or '', + 'scope': 'title' if self.scope_title else 'any', + 'authors': self.author or '', + 'pub': self.pub or '', + 'ylo': self.timeframe[0] or '', + 'yhi': self.timeframe[1] or '', + 'patents': '0' if self.include_patents else '1', + 'citations': '0' if self.include_citations else '1'} + + for key, val in urlargs.items(): + urlargs[key] = quote(encode(val)) + + # The following URL arguments must not be quoted, or the + # server will not recognize them: + urlargs['num'] = ('&num=%d' % self.num_results + if self.num_results is not None else '') + + return self.SCHOLAR_QUERY_URL % urlargs + + +class ScholarSettings(object): + """ + This class lets you adjust the Scholar settings for your + session. It's intended to mirror the features tunable in the + Scholar Settings pane, but right now it's a bit basic. + """ + CITFORM_NONE = 0 + CITFORM_REFWORKS = 1 + CITFORM_REFMAN = 2 + CITFORM_ENDNOTE = 3 + CITFORM_BIBTEX = 4 + + def __init__(self): + self.citform = 0 # Citation format, default none + self.per_page_results = None + self._is_configured = False + + def set_citation_format(self, citform): + citform = ScholarUtils.ensure_int(citform) + if citform < 0 or citform > self.CITFORM_BIBTEX: + raise FormatError('citation format invalid, is "%s"' + % citform) + self.citform = citform + self._is_configured = True + + def set_per_page_results(self, per_page_results): + self.per_page_results = ScholarUtils.ensure_int( + per_page_results, 'page results must be integer') + self.per_page_results = min( + self.per_page_results, ScholarConf.MAX_PAGE_RESULTS) + self._is_configured = True + + def is_configured(self): + return self._is_configured + + +class ScholarQuerier(object): + """ + ScholarQuerier instances can conduct a search on Google Scholar + with subsequent parsing of the resulting HTML content. The + articles found are collected in the articles member, a list of + ScholarArticle instances. + """ + + # Default URLs for visiting and submitting Settings pane, as of 3/14 + GET_SETTINGS_URL = ScholarConf.SCHOLAR_SITE + '/scholar_settings?' \ + + 'sciifh=1&hl=en&as_sdt=0,5' + + SET_SETTINGS_URL = ScholarConf.SCHOLAR_SITE + '/scholar_setprefs?' \ + + 'q=' \ + + '&scisig=%(scisig)s' \ + + '&inststart=0' \ + + '&as_sdt=1,5' \ + + '&as_sdtp=' \ + + '&num=%(num)s' \ + + '&scis=%(scis)s' \ + + '%(scisf)s' \ + + '&hl=en&lang=all&instq=&inst=569367360547434339&save=' + + # Older URLs: + # ScholarConf.SCHOLAR_SITE + '/scholar?q=%s&hl=en&btnG=Search&as_sdt=2001&as_sdtp=on + + class Parser(ScholarArticleParser120726): + def __init__(self, querier): + ScholarArticleParser120726.__init__(self) + self.querier = querier + + def handle_num_results(self, num_results): + if self.querier is not None and self.querier.query is not None: + self.querier.query['num_results'] = num_results + + def handle_article(self, art): + self.querier.add_article(art) + + def __init__(self): + self.articles = [] + self.query = None + self.cjar = MozillaCookieJar() + + # If we have a cookie file, load it: + if ScholarConf.COOKIE_JAR_FILE and \ + os.path.exists(ScholarConf.COOKIE_JAR_FILE): + try: + self.cjar.load(ScholarConf.COOKIE_JAR_FILE, + ignore_discard=True) + ScholarUtils.log('info', 'loaded cookies file') + except Exception as msg: + ScholarUtils.log('warn', 'could not load cookies file: %s' % msg) + self.cjar = MozillaCookieJar() # Just to be safe + + self.opener = build_opener(HTTPCookieProcessor(self.cjar)) + self.settings = None # Last settings object, if any + + def apply_settings(self, settings): + """ + Applies settings as provided by a ScholarSettings instance. + """ + if settings is None or not settings.is_configured(): + return True + + self.settings = settings + + # This is a bit of work. We need to actually retrieve the + # contents of the Settings pane HTML in order to extract + # hidden fields before we can compose the query for updating + # the settings. + html = self._get_http_response(url=self.GET_SETTINGS_URL, + log_msg='dump of settings form HTML', + err_msg='requesting settings failed') + if html is None: + return False + + # Now parse the required stuff out of the form. We require the + # "scisig" token to make the upload of our settings acceptable + # to Google. + soup = SoupKitchen.make_soup(html) + + tag = soup.find(name='form', attrs={'id': 'gs_settings_form'}) + if tag is None: + ScholarUtils.log('info', 'parsing settings failed: no form') + return False + + tag = tag.find('input', attrs={'type':'hidden', 'name':'scisig'}) + if tag is None: + ScholarUtils.log('info', 'parsing settings failed: scisig') + return False + + urlargs = {'scisig': tag['value'], + 'num': settings.per_page_results, + 'scis': 'no', + 'scisf': ''} + + if settings.citform != 0: + urlargs['scis'] = 'yes' + urlargs['scisf'] = '&scisf=%d' % settings.citform + + html = self._get_http_response(url=self.SET_SETTINGS_URL % urlargs, + log_msg='dump of settings result HTML', + err_msg='applying setttings failed') + if html is None: + return False + + ScholarUtils.log('info', 'settings applied') + return True + + def send_query(self, query): + """ + This method initiates a search query (a ScholarQuery instance) + with subsequent parsing of the response. + """ + self.clear_articles() + self.query = query + + html = self._get_http_response(url=query.get_url(), + log_msg='dump of query response HTML', + err_msg='results retrieval failed') + if html is None: + return + + self.parse(html) + + def get_citation_data(self, article): + """ + Given an article, retrieves citation link. Note, this requires that + you adjusted the settings to tell Google Scholar to actually + provide this information, *prior* to retrieving the article. + """ + if article['url_citation'] is None: + return False + if article.citation_data is not None: + return True + + ScholarUtils.log('info', 'retrieving citation export data') + data = self._get_http_response(url=article['url_citation'], + log_msg='citation data response', + err_msg='requesting citation data failed') + if data is None: + return False + + article.set_citation_data(data) + return True + + def parse(self, html): + """ + This method allows parsing of provided HTML content. + """ + parser = self.Parser(self) + parser.parse(html) + + def add_article(self, art): + self.get_citation_data(art) + self.articles.append(art) + + def clear_articles(self): + """Clears any existing articles stored from previous queries.""" + self.articles = [] + + def save_cookies(self): + """ + This stores the latest cookies we're using to disk, for reuse in a + later session. + """ + if ScholarConf.COOKIE_JAR_FILE is None: + return False + try: + self.cjar.save(ScholarConf.COOKIE_JAR_FILE, + ignore_discard=True) + ScholarUtils.log('info', 'saved cookies file') + return True + except Exception as msg: + ScholarUtils.log('warn', 'could not save cookies file: %s' % msg) + return False + + def _get_http_response(self, url, log_msg=None, err_msg=None): + """ + Helper method, sends HTTP request and returns response payload. + """ + if log_msg is None: + log_msg = 'HTTP response data follow' + if err_msg is None: + err_msg = 'request failed' + try: + ScholarUtils.log('info', 'requesting %s' % unquote(url)) + + req = Request(url=url, headers={'User-Agent': ScholarConf.USER_AGENT}) + hdl = self.opener.open(req) + html = hdl.read() + + ScholarUtils.log('debug', log_msg) + ScholarUtils.log('debug', '>>>>' + '-'*68) + ScholarUtils.log('debug', 'url: %s' % hdl.geturl()) + ScholarUtils.log('debug', 'result: %s' % hdl.getcode()) + ScholarUtils.log('debug', 'headers:\n' + str(hdl.info())) + ScholarUtils.log('debug', 'data:\n' + html.decode('utf-8')) # For Python 3 + ScholarUtils.log('debug', '<<<<' + '-'*68) + + return html + except Exception as err: + ScholarUtils.log('info', err_msg + ': %s' % err) + return None + + +def txt(querier, with_globals): + if with_globals: + # If we have any articles, check their attribute labels to get + # the maximum length -- makes for nicer alignment. + max_label_len = 0 + if len(querier.articles) > 0: + items = sorted(list(querier.articles[0].attrs.values()), + key=lambda item: item[2]) + max_label_len = max([len(str(item[1])) for item in items]) + + # Get items sorted in specified order: + items = sorted(list(querier.query.attrs.values()), key=lambda item: item[2]) + # Find largest label length: + max_label_len = max([len(str(item[1])) for item in items] + [max_label_len]) + fmt = '[G] %%%ds %%s' % max(0, max_label_len-4) + for item in items: + if item[0] is not None: + print(fmt % (item[1], item[0])) + if len(items) > 0: + print + + articles = querier.articles + for art in articles: + print(encode(art.as_txt()) + '\n') + +def csv(querier, header=False, sep='|'): + articles = querier.articles + for art in articles: + result = art.as_csv(header=header, sep=sep) + print(encode(result)) + header = False + +def citation_export(querier): + articles = querier.articles + for art in articles: + print(art.as_citation() + '\n') + + +def main(): + usage = """scholar.py [options] <query string> +A command-line interface to Google Scholar. + +Examples: + +# Retrieve one article written by Einstein on quantum theory: +scholar.py -c 1 --author "albert einstein" --phrase "quantum theory" + +# Retrieve a BibTeX entry for that quantum theory paper: +scholar.py -c 1 -C 17749203648027613321 --citation bt + +# Retrieve five articles written by Einstein after 1970 where the title +# does not contain the words "quantum" and "theory": +scholar.py -c 5 -a "albert einstein" -t --none "quantum theory" --after 1970""" + + fmt = optparse.IndentedHelpFormatter(max_help_position=50, width=100) + parser = optparse.OptionParser(usage=usage, formatter=fmt) + group = optparse.OptionGroup(parser, 'Query arguments', + 'These options define search query arguments and parameters.') + group.add_option('-a', '--author', metavar='AUTHORS', default=None, + help='Author name(s)') + group.add_option('-A', '--all', metavar='WORDS', default=None, dest='allw', + help='Results must contain all of these words') + group.add_option('-s', '--some', metavar='WORDS', default=None, + help='Results must contain at least one of these words. Pass arguments in form -s "foo bar baz" for simple words, and -s "a phrase, another phrase" for phrases') + group.add_option('-n', '--none', metavar='WORDS', default=None, + help='Results must contain none of these words. See -s|--some re. formatting') + group.add_option('-p', '--phrase', metavar='PHRASE', default=None, + help='Results must contain exact phrase') + group.add_option('-t', '--title-only', action='store_true', default=False, + help='Search title only') + group.add_option('-P', '--pub', metavar='PUBLICATIONS', default=None, + help='Results must have appeared in this publication') + group.add_option('--after', metavar='YEAR', default=None, + help='Results must have appeared in or after given year') + group.add_option('--before', metavar='YEAR', default=None, + help='Results must have appeared in or before given year') + group.add_option('--no-patents', action='store_true', default=False, + help='Do not include patents in results') + group.add_option('--no-citations', action='store_true', default=False, + help='Do not include citations in results') + group.add_option('-C', '--cluster-id', metavar='CLUSTER_ID', default=None, + help='Do not search, just use articles in given cluster ID') + group.add_option('-c', '--count', type='int', default=None, + help='Maximum number of results') + parser.add_option_group(group) + + group = optparse.OptionGroup(parser, 'Output format', + 'These options control the appearance of the results.') + group.add_option('--txt', action='store_true', + help='Print article data in text format (default)') + group.add_option('--txt-globals', action='store_true', + help='Like --txt, but first print global results too') + group.add_option('--csv', action='store_true', + help='Print article data in CSV form (separator is "|")') + group.add_option('--csv-header', action='store_true', + help='Like --csv, but print header with column names') + group.add_option('--citation', metavar='FORMAT', default=None, + help='Print article details in standard citation format. Argument Must be one of "bt" (BibTeX), "en" (EndNote), "rm" (RefMan), or "rw" (RefWorks).') + parser.add_option_group(group) + + group = optparse.OptionGroup(parser, 'Miscellaneous') + group.add_option('--cookie-file', metavar='FILE', default=None, + help='File to use for cookie storage. If given, will read any existing cookies if found at startup, and save resulting cookies in the end.') + group.add_option('-d', '--debug', action='count', default=0, + help='Enable verbose logging to stderr. Repeated options increase detail of debug output.') + group.add_option('-v', '--version', action='store_true', default=False, + help='Show version information') + parser.add_option_group(group) + + options, _ = parser.parse_args() + + # Show help if we have neither keyword search nor author name + if len(sys.argv) == 1: + parser.print_help() + return 1 + + if options.debug > 0: + options.debug = min(options.debug, ScholarUtils.LOG_LEVELS['debug']) + ScholarConf.LOG_LEVEL = options.debug + ScholarUtils.log('info', 'using log level %d' % ScholarConf.LOG_LEVEL) + + if options.version: + print('This is scholar.py %s.' % ScholarConf.VERSION) + return 0 + + if options.cookie_file: + ScholarConf.COOKIE_JAR_FILE = options.cookie_file + + # Sanity-check the options: if they include a cluster ID query, it + # makes no sense to have search arguments: + if options.cluster_id is not None: + if options.author or options.allw or options.some or options.none \ + or options.phrase or options.title_only or options.pub \ + or options.after or options.before: + print('Cluster ID queries do not allow additional search arguments.') + return 1 + + querier = ScholarQuerier() + settings = ScholarSettings() + + if options.citation == 'bt': + settings.set_citation_format(ScholarSettings.CITFORM_BIBTEX) + elif options.citation == 'en': + settings.set_citation_format(ScholarSettings.CITFORM_ENDNOTE) + elif options.citation == 'rm': + settings.set_citation_format(ScholarSettings.CITFORM_REFMAN) + elif options.citation == 'rw': + settings.set_citation_format(ScholarSettings.CITFORM_REFWORKS) + elif options.citation is not None: + print('Invalid citation link format, must be one of "bt", "en", "rm", or "rw".') + return 1 + + querier.apply_settings(settings) + + if options.cluster_id: + query = ClusterScholarQuery(cluster=options.cluster_id) + else: + query = SearchScholarQuery() + if options.author: + query.set_author(options.author) + if options.allw: + query.set_words(options.allw) + if options.some: + query.set_words_some(options.some) + if options.none: + query.set_words_none(options.none) + if options.phrase: + query.set_phrase(options.phrase) + if options.title_only: + query.set_scope(True) + if options.pub: + query.set_pub(options.pub) + if options.after or options.before: + query.set_timeframe(options.after, options.before) + if options.no_patents: + query.set_include_patents(False) + if options.no_citations: + query.set_include_citations(False) + + if options.count is not None: + options.count = min(options.count, ScholarConf.MAX_PAGE_RESULTS) + query.set_num_page_results(options.count) + + querier.send_query(query) + + if options.csv: + csv(querier) + elif options.csv_header: + csv(querier, header=True) + elif options.citation is not None: + citation_export(querier) + else: + txt(querier, with_globals=options.txt_globals) + + if options.cookie_file: + querier.save_cookies() + + return 0 + +if __name__ == "__main__": + sys.exit(main()) |
