Face recognition: A literature survey

By Wen Zhao, Ramalingam Chellappa, P. Jonathon Phillips, Azriel Rosenfeld. CSUR, 2003

7,411 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/28312c3a47c1be3a67365700744d3d6665b86f22

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In Computer Vision and Pattern Recognition

Citations in the corpus

Face Recognition Based on Fitting a 3D Morphable Model (2003, methodology): “Variations in pose and illumination, which may produce changes larger than the differences between different people's images, are the main challenge for face recognition [39].”
Deep Face Recognition: A Survey (2018, background): “Over the past few years, there have been several surveys on FR [222], [18], [3], [78], [136] and its subdomains, including illumination-invariant FR [234], 3D FR [136], pose-invariant FR [216] and so on.”
Robust representations for face recognition: the power of averages. (2005, background): “The system is tested on a range of images, which are much more heterogeneous than normally used in reports of automatic recognition systems (Phillips et al, 2000; Zhao et al, 2003).”
Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval (2023, background): “Image retrieval brings great convenience to our daily life in various areas such as e-commerce search [8, 16], face recognition [17, 32], and landmark localization [13, 24].”
Neighborhood repulsed metric learning for kinship verification (2012, background): “Representative examples including face recognition [2, 15, 21, 22, 29], facial expression recognition [6], age estimation [7, 9, 14], gender classification [17] and ethnicity recognition [10].”
Discriminative common vectors for face recognition (2005, methodology): “Many methods have been proposed for face recognition within the last two decades [1], [3].”
Learning Compact Binary Face Descriptor for Face Recognition (2015, background): “However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand.”
Face recognition using Histograms of Oriented Gradients (2011, background): “The latter seem to have prevailed in the literature (Zhao et al., 2003).”
Linear discriminant analysis for the small sample size problem: an overview (2014, neutral): “…(Paliwal and Sharma, 2011), PCA+LDA (Swets and Weng, 1996; Belhumer et al., 1997), regularized LDA (RLDA) (Friedman, 1989; Lu et al., 2003b, 2005; Zhao et al., 1998, 1999, 2003), eigenfeature regularization (EFR) (Jiang et al., 2008), extrapolation of scatter matrices (ELDA) (Sharma and Paliwal,…”
FRVT 2006 and ICE 2006 large-scale results (2007, neutral): “The identification of any commercial product or trade name does not imply endorsement or recommendation by the National Institute of Standards and Technology, SAIC, Schafer Corp., U. of Texas at Dallas or U. of Notre Dame.”
Learning Discriminant Face Descriptor (2014, background): “Up to now, many face representation approaches have been introduced, including subspace-based holistic features and local appearance features [8], [9].”
Probabilistic recognition of human faces from video (2002, background): “Though significant research has been conducted on still-to-still recognition, research efforts on still-to-video recognition, are relatively fewer due to the following challenges [7] in typical surveillance applications: poor video quality, significant illumination and pose variations, and low image”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, neutral): “Output Raw images”
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition (2005, background): “Within the past two decades, numerous FR algorithms have been proposed, and detailed surveys of the developments in the area have appeared in the literature (see e.g. Samal and Iyengar, 1992; Valentin et al., 1994; Chellappa et al., 1995; Gong et al., 2000; Turk, 2001; Zhao et al., 2003).”
Face Recognition with Local Binary Patterns (2004, background): “The availability of numerous commercial face recognition systems [1] attests to the significant progress achieved in the research field [2].”
Combining Face and Iris Biometrics for Identity Verification (2003, background): “As we know, a face recognition system includes face detection, preprocessing, feature extraction and matching [3].”
Gabor-Based Region Covariance Matrices for Face Recognition (2008, background): “In general, there are two main approaches to face recognition: geometric feature based and template based [22].”
Discriminative K-SVD for dictionary learning in face recognition (2010, background): “Face recognition is a challenging computer vision task that has seen active research for many years [23].”
Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, background): “beyond the scope of this paper, and we refer the readers to the survey papers/books [9], [10] for a comprehensive review of”