The FERET database and evaluation procedure for face-recognition algorithms

By P. Phillips, Harry Wechsler, Jeff Huang, Patrick J. Rauss. 1998

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Citations in the corpus

Multi-class texture analysis in colorectal cancer histology (2016, background): “Whereas publicly available benchmarking datasets exist for image classification problems such as face recognition 17 , handwriting recognition 18 , universal computer vision problems 19 and texture classification 20,21 , no such data are available for histopathological tissue classification.”
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions (2009, background): “Our next example involves a large, dense matrix derived from the FERET databank of face images [107, 108].”
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction (2020, background): “[93] 2020 [94], [95], [96], and [97] sparse representation and discriminative projection -”
Probabilistic Linear Discriminant Analysis (2006, methodology): “Each test case consists of a gallery containing one example of each of M people from the FERET database [9] (the training data was collected by us and did not include any FERET images).”
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition (2009, methodology): “In this sub-section, we formulate weight learning as a discriminant analysis problem, in which the weighted sum can be considered as linear projection from -”
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition (2002, methodology): “We assessed the feasibility and performance of our novel Gabor-Fisher Classifier (GFC) on the face recognition task, using a data set from the FERET database, which is a standard testbed for face recognition technologies [31], [2].”
High-speed face recognition based on discrete cosine transform and RBF neural networks (2005, methodology): “The proposed feature extraction method is also tested on the FERET database which contains more subjects with different variations [37].”
The BANCA Database and Evaluation Protocol (2003, methodology): “In the rest of this paper the following notation will be used: X i : subject i in group g g ∈ {g1, g2}, i ∈ [1, 13] yk(X) : true client record from session k by subject X k ∈ [1, 12] zl(X) : impostor record (from a subject X ′) claiming identity X during a session l (with X ′ = X) l ∈ [1, 12]”
Past, Present, and Future of Face Recognition: A Review (2020, methodology): “ORL [23] 1994 400 40 10 FERET [13] 1996 14,126 1199 AR [24] 1998 3016 116 26 XM2VTS [25] 1999 - 295 BANCA [26] 2003 - 208 FRGC [14] 2006 50,000 7 LFW [10] 2007 13,233 5749 ≈2.”
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, methodology): “PCA and LDA are two well-known linear subspace learning methods which have been extensively used in pattern recognition and computer vision areas and have become the most popular techniques for face recognition and other biometrics [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]”
Face Recognition with Local Binary Patterns (2004, methodology): “To facilitate this task, the FERET database and evaluation methodology have been created [3].”
Solving the small sample size problem of LDA (2002, methodology): “To experiment on more challenging data, we have selected 70 subjects from the FERET database [8] with 6 up-right, frontal-view images of each subject.”
(2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition (2005, unknown): “and alternative 2DPCA, on two well-known face databases: ORL and FERET [ 5 ].”
PCCA: A new approach for distance learning from sparse pairwise constraints (2012, background): “While face verification [16, 23] is of deciding whether two face images (e.”