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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
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+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
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+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
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+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/Attribute and Simile Classifiers for Face Verification.csv b/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv
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+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
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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/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/Comprehensive Database for Facial Expression Analysis.csv b/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv
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+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/Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics.csv b/datasets/scholar/entries/Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics.csv
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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
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+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/Face Research Lab London Set. figshare.csv b/datasets/scholar/entries/Face Research Lab London Set. figshare.csv
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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
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+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/How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia.csv b/datasets/scholar/entries/How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia.csv
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+++ b/datasets/scholar/entries/How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia.csv
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
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+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/Level Playing Field for Million Scale Face Recognition.csv b/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv
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+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/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
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+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/Names and Faces in the News.csv b/datasets/scholar/entries/Names and Faces in the News.csv
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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
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+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/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv b/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv
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+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/THUMOS Challenge: Action Recognition with a Large Number of Classes.csv b/datasets/scholar/entries/THUMOS Challenge: Action Recognition with a Large Number of Classes.csv
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