Recognition of faces in unconstrained environments: a comparative study
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Image and Vision Computing
Compact binary patterns (CBP) with multiple patch classifiers for fast and accurate face recognition
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Block-based Deep Belief Networks for face recognition
International Journal of Biometrics
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Face representations based on Gabor features have achieved great success in face recognition, such as Elastic Graph Matching, Gabor Fisher Classifier (GFC), and AdaBoosted Gabor Fisher Classifier (AGFC). In GFC and AGFC, either down-sampled or selected Gabor features are analyzed in holistic mode by a single classifier. In this paper, we propose a novel patch-based GFC (PGFC) method, in which Gabor features are spatially partitioned into a number of patches, and on each patch one GFC is constructed as component classifier to form the final ensemble classifier using sum rule. The positions and sizes of the patches are learned from a training data using AdaBoost. Experiments on two large-scale face databases (FERET and CAS-PEAL-R1) show that the proposed PGFC with only tens of patches outperforms the GFC and AGFC impressively.