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Principal patterns of fractional-order differential gradients for face recognition

[+] Author Affiliations
Lei Yu

Chongqing Normal University, College of Computer and Information Science, West Road of University Town, Chongqing 401331, China

Qi Cao

Logistical Engineering University, Department of Training, South Road of University Town, Chongqing 401311, China

Anping Zhao

Chongqing Normal University, College of Computer and Information Science, West Road of University Town, Chongqing 401331, China

J. Electron. Imaging. 24(1), 013021 (Jan 27, 2015). doi:10.1117/1.JEI.24.1.013021
History: Received September 3, 2014; Accepted December 29, 2014
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Abstract.  We investigate the ability of fractional-order differentiation (FD) for facial texture representation and present a local descriptor, called the principal patterns of fractional-order differential gradients (PPFDGs), for face recognition. In PPFDG, multiple FD gradient patterns of a face image are obtained utilizing multiorientation FD masks. As a result, each pixel of the face image can be represented as a high-dimensional gradient vector. Then, by employing principal component analysis to the gradient vectors over the centered neighborhood of each pixel, we capture the principal gradient patterns and meanwhile compute the corresponding orientation patterns from which oriented gradient magnitudes are computed. Histogram features are finally extracted from these oriented gradient magnitude patterns as the face representation using local binary patterns. Experimental results on face recognition technology, A.M. Martinez and R. Benavente, Extended Yale B, and labeled faces in the wild face datasets validate the effectiveness of the proposed method.

© 2015 SPIE and IS&T

Citation

Lei Yu ; Qi Cao and Anping Zhao
"Principal patterns of fractional-order differential gradients for face recognition", J. Electron. Imaging. 24(1), 013021 (Jan 27, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013021


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