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diff --git a/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv new file mode 100644 index 00000000..eaaf1a93 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv b/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv new file mode 100644 index 00000000..c1bf1f38 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv b/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv new file mode 100644 index 00000000..31bf7b39 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv b/scraper/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv new file mode 100644 index 00000000..1d6e856b --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv b/scraper/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv new file mode 100644 index 00000000..074471b7 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv b/scraper/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv new file mode 100644 index 00000000..0b36206c --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv b/scraper/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv new file mode 100644 index 00000000..36b9e0cf --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv b/scraper/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv new file mode 100644 index 00000000..e97d2c56 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/scraper/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv new file mode 100644 index 00000000..b3728548 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv b/scraper/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv new file mode 100644 index 00000000..5cd26552 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv b/scraper/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv new file mode 100644 index 00000000..252d269c --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv b/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv new file mode 100644 index 00000000..eeb0adb1 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/scraper/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv new file mode 100644 index 00000000..2f1e41af --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv b/scraper/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv new file mode 100644 index 00000000..de202138 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv b/scraper/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv new file mode 100644 index 00000000..43da8a92 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv b/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv new file mode 100644 index 00000000..249cea3a --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv b/scraper/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/scraper/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/scraper/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/scraper/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/scraper/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/scraper/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv b/scraper/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv new file mode 100644 index 00000000..f7130a67 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv new file mode 100644 index 00000000..0fa7a800 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv b/scraper/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv new file mode 100644 index 00000000..a41ffc41 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv b/scraper/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv new file mode 100644 index 00000000..89746fe9 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv b/scraper/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv new file mode 100644 index 00000000..c5540c9a --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv b/scraper/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv new file mode 100644 index 00000000..d632534a --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv new file mode 100644 index 00000000..c1245878 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv b/scraper/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv new file mode 100644 index 00000000..9215c287 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv b/scraper/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv new file mode 100644 index 00000000..503356df --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv b/scraper/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv new file mode 100644 index 00000000..6fa46797 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv b/scraper/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv new file mode 100644 index 00000000..68dc8389 --- /dev/null +++ b/scraper/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/scraper/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv b/scraper/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/scraper/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/scraper/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv b/scraper/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv new file mode 100644 index 00000000..adf44ba7 --- /dev/null +++ b/scraper/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. We also introduce … |