Principal patterns of fractional-order differential gradients for face recognition

Lei Yu; Qi Cao; Anping Zhao

doi:10.1117/1.JEI.24.1.013021

27 January 2015 Principal patterns of fractional-order differential gradients for face recognition

Lei Yu, Qi Cao, Anping Zhao

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013021 (January 2015). https://doi.org/10.1117/1.JEI.24.1.013021

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.

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Lei Yu, Qi Cao, and Anping Zhao "Principal patterns of fractional-order differential gradients for face recognition," Journal of Electronic Imaging 24(1), 013021 (27 January 2015). https://doi.org/10.1117/1.JEI.24.1.013021

Published: 27 January 2015

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