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diff --git a/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv
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+300 faces in-the-wild challenge: The first facial landmark localization challenge|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|2013|396|15|7861246476672124064|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|http://scholar.google.com/scholar?cites=7861246476672124064&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7861246476672124064&hl=en&as_sdt=0,5|None|Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well as the lack of a evaluation protocol makes it difficult to compare performance between different systems. In this paper, we present the …
diff --git a/datasets/scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv b/datasets/scholar/entries/Age and Gender Estimation of Unfiltered Faces.csv
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diff --git a/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv
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+Dex: Deep expectation of apparent age from a single image|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.html|2015|155|15|12384435539194835187|None|http://scholar.google.com/scholar?cites=12384435539194835187&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12384435539194835187&hl=en&as_sdt=0,5|None|In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the …
diff --git a/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv b/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv
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+Deep expectation of real and apparent age from a single image without facial landmarks|http://scholar.google.com/https://link.springer.com/article/10.1007/s11263-016-0940-3|2018|135|7|11164967779616636427|None|http://scholar.google.com/scholar?cites=11164967779616636427&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11164967779616636427&hl=en&as_sdt=0,5|None|In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for …
diff --git a/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv b/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv
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+Distance estimation of an unknown person from a portrait|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-10590-1_21|2014|7|9|11199246855168438175|None|http://scholar.google.com/scholar?cites=11199246855168438175&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11199246855168438175&hl=en&as_sdt=0,5|None|We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each …
diff --git a/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv
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+Face recognition in unconstrained videos with matched background similarity|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5995566/|2011|657|10|5401801956686441353|None|http://scholar.google.com/scholar?cites=5401801956686441353&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5401801956686441353&hl=en&as_sdt=0,5|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions.(a) We present a comprehensive …
+Face Recognition in Unconstrained Videos with Matched Background Similarity|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|2012|0|0|None|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|None|None|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this work we make the following contributions:(a) We present a comprehensive …
diff --git a/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv b/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv
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+Face detection, pose estimation, and landmark localization in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6248014/|2012|1693|7|4876235110904982186|None|http://scholar.google.com/scholar?cites=4876235110904982186&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4876235110904982186&hl=en&as_sdt=0,5|None|We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a …
diff --git a/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv b/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv
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+From facial parts responses to face detection: A deep learning approach|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Yang_From_Facial_Parts_ICCV_2015_paper.html|2015|213|12|1818335115841631894|None|http://scholar.google.com/scholar?cites=1818335115841631894&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1818335115841631894&hl=en&as_sdt=0,5|None|In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging …
diff --git a/datasets/scholar/entries/From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose.csv b/datasets/scholar/entries/From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose.csv
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diff --git a/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv b/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv
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+Labeled faces in the wild: A database forstudying face recognition in unconstrained environments|http://scholar.google.com/https://hal.inria.fr/inria-00321923/|2008|3014|37|6713997626354918066|None|http://scholar.google.com/scholar?cites=6713997626354918066&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6713997626354918066&hl=en&as_sdt=0,5|None|Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is …
diff --git a/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv
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+Large age-gap face verification by feature injection in deep networks|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0167865517300727|2017|12|8|6980699793307007950|None|http://scholar.google.com/scholar?cites=6980699793307007950&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6980699793307007950&hl=en&as_sdt=0,5|None|This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection …
diff --git a/datasets/scholar/entries/Learning Face Representation from Scratch.csv b/datasets/scholar/entries/Learning Face Representation from Scratch.csv
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diff --git a/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv
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+Localizing parts of faces using a consensus of exemplars|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6412675/|2013|740|13|8801930631236620204|None|http://scholar.google.com/scholar?cites=8801930631236620204&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8801930631236620204&hl=en&as_sdt=0,5|None|We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and …
diff --git a/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv b/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv
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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/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv b/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv
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+Ms-celeb-1m: A dataset and benchmark for large-scale face recognition|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-46487-9_6|2016|189|6|7096719334274798105|None|http://scholar.google.com/scholar?cites=7096719334274798105&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7096719334274798105&hl=en&as_sdt=0,5|None|In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world …
diff --git a/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv b/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv
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+Presentation and validation of the Radboud Faces Database|http://scholar.google.com/https://www.tandfonline.com/doi/abs/10.1080/02699930903485076|2010|1052|10|9283473996856444608|None|http://scholar.google.com/scholar?cites=9283473996856444608&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9283473996856444608&hl=en&as_sdt=0,5|None|Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several …
diff --git a/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv b/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv
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+Spoofing faces using makeup: An investigative study|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7947686/|2017|6|8|1291042674502639294|None|http://scholar.google.com/scholar?cites=1291042674502639294&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1291042674502639294&hl=en&as_sdt=0,5|None|Makeup can be used to alter the facial appearance of a person. Previous studies have established the potential of using makeup to obfuscate the identity of an individual with respect to an automated face matcher. In this work, we analyze the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another person's facial appearance. In this regard, we first assemble a set of face images downloaded from the internet where individuals use facial cosmetics to impersonate …
diff --git a/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv b/datasets/scholar/entries/The CMU Pose, Illumination, and Expression Database.csv
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diff --git a/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv b/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv
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+The Do's and Don'ts for CNN-based Face Verification.|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|2017|21|7|16583671830808674747|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|http://scholar.google.com/scholar?cites=16583671830808674747&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=16583671830808674747&hl=en&as_sdt=0,5|None|While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered. In this paper, we explore the following questions that are critical to face recognition research:(i) Can we train on still images and expect the systems to work on videos?(ii) Are deeper datasets better than wider datasets?(iii) Does adding label noise lead to improvement in performance of deep networks?(iv) Is alignment needed for face …
diff --git a/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv b/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv
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+The extended cohn-kanade dataset (ck+) a complete expression dataset for action unit and emotion-speified expression|None|2010|3|0|13817454793240235261|None|http://scholar.google.com/scholar?cites=13817454793240235261&as_sdt=2005&sciodt=0,5&hl=en|None|None|None
diff --git a/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv b/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv
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+The megaface benchmark: 1 million faces for recognition at scale|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.html|2016|159|11|6051410257476935491|None|http://scholar.google.com/scholar?cites=6051410257476935491&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6051410257476935491&hl=en&as_sdt=0,5|None|Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms …
diff --git a/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv b/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv
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+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 …
diff --git a/datasets/scholar/entries/Who's in the Picture .csv b/datasets/scholar/entries/Who's in the Picture .csv
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