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diff --git a/datasets/scholar/entries/AgeDB: the first manually collected, in-the-wild age database.csv b/datasets/scholar/entries/AgeDB: the first manually collected, in-the-wild age database.csv
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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
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+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/Automatic Facial Makeup Detection with Application in Face Recognition.csv b/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv
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+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
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+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
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+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/Distance Estimation of an Unknown Person from a Portrait.csv b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait.csv
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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
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+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 Swapping: Automatically Replacing Faces in Photographs.csv b/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv
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+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/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
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+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/Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction.csv b/datasets/scholar/entries/Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction.csv
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diff --git a/datasets/scholar/entries/Hipster Wars: Discovering Elements of Fashion Styles..csv b/datasets/scholar/entries/Hipster Wars: Discovering Elements of Fashion Styles..csv
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diff --git a/datasets/scholar/entries/Interactive Facial Feature Localization.csv b/datasets/scholar/entries/Interactive Facial Feature Localization.csv
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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
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+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/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
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+++ b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv
diff --git a/datasets/scholar/entries/Names and Faces .csv b/datasets/scholar/entries/Names and Faces .csv
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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
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+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/Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A.csv b/datasets/scholar/entries/Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A.csv
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diff --git a/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv
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+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/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
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+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 MUCT Landmarked Face Database.csv b/datasets/scholar/entries/The MUCT Landmarked Face Database.csv
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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
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+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/WIDER FACE: A Face Detection Benchmark.csv b/datasets/scholar/entries/WIDER FACE: A Face Detection Benchmark.csv
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+++ b/datasets/scholar/entries/WIDER FACE: A Face Detection Benchmark.csv