The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

By Wen Gao, Bo Cao, Shiguang Shan, Xilin Chen, Delong Zhou, Xiaohua Zhang, Debin Zhao. IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 2008

1,042 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2485c98aa44131d1a2f7d1355b1e372f2bb148ad

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Citations in the corpus

Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor (2010, methodology): “In Section IV, extensive experiments on FERET [20], CAS-PEAL [7], CMU-PIE [23], Extended Yale B [9], [14], and FRGC [19] databases are conducted to evaluate the performance of the proposed method on face recognition.”
Hallucinating face by position-patch (2010, methodology): “We randomly selected 300 normal expression images of different people under the same light condition in CAS-PEAL Face Database [16].”
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments (2008, background): “For example, while there are large numbers of images with uncontrolled lighting in the FRGC data sets, these images contain a great deal less natural variation than the Database # of people Total images Highlights References AR Face Database, Purdue University, USA 126 4000 frontal pose, expression,”
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis (2013, neutral): “, 2001), 9,030 from CAS-PEAL (Gao et al., 2008), 5,479 from Caltech (Fink et al.”
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, background): “Index Terms—Face recognition, face image descriptors, face image representation F”
Learning Compact Binary Face Descriptor for Face Recognition (2015, methodology): “We evaluate our CBFD and C-CBFD methods on the widely used FERET [46], CAS-PEAL-R1 [13], LFW [22], PaSC [6], and CASIA NIR-VIS 2.0 [33] face datasets.”
Very low resolution face recognition problem (2010, methodology): “Database and Settings: Public face databases CAS-PEAL [5] and YaleB [6] are selected for the experiments.”
Local Directional Number Pattern for Face Analysis: Face and Expression Recognition (2013, methodology): “We performed several experiments to evaluate the performance of the proposed coding scheme for face recognition and expression classiﬁcation.”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “PUBFIG, which used the labels “man” and “attractive woman,” used Amazon Mechanical Turk to crowdsource gender labels: Each job was submitted to 3 different workers and only labels where all 3 people agreed were used.”
Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization (2011, background): “Other large-scale databases such as Caltech 10,000 Web Faces [2], CAS-PEAL Face Database [11] or the CMU / VASC [27] databases are designed for face detection .”
A Comprehensive Survey to Face Hallucination (2013, methodology): “To overcome these defects, Gao et al. (2008c) extended the aforementioned method to local patch-based sketch synthesis in a subsequent work.”
Extended local binary patterns for face recognition (2016, methodology): “Chi square+NNC vs. WPCA+Euclidean+NNC; For the LBP_S and LBP , we used as done in previous work [1,5,6] .”