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IEEE Transactions on Image Processing
Face recognition in global harmonic subspace
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Face recognition with patterns of oriented edge magnitudes
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Kernel fusion of multiple histogram descriptors for robust face recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Gaze data collection with the off-the-shelf devices
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Local Kernel Feature Analysis (LKFA) for object recognition
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ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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Pattern Recognition
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Knowledge-Based Systems
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Journal of Electrical and Computer Engineering
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Pattern Recognition Letters
Fast multi-scale local phase quantization histogram for face recognition
Pattern Recognition Letters
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Pattern Recognition
Relative phase in dual tree shearlets
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A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate