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Journal of Electronic Imaging

Face recognition with histograms of fractional differential gradients
Author(s): Lei Yu; Yan Ma; Qi Cao
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Paper Abstract

It has proved that fractional differentiation can enhance the edge information and nonlinearly preserve textural detailed information in an image. This paper investigates its ability for face recognition and presents a local descriptor called histograms of fractional differential gradients (HFDG) to extract facial visual features. HFDG encodes a face image into gradient patterns using multiorientation fractional differential masks, from which histograms of gradient directions are computed as the face representation. Experimental results on Yale, face recognition technology (FERET), Carnegie Mellon University pose, illumination, and expression (CMU PIE), and A. Martinez and R. Benavente (AR) databases validate the feasibility of the proposed method and show that HFDG outperforms local binary patterns (LBP), histograms of oriented gradients (HOG), enhanced local directional patterns (ELDP), and Gabor feature-based methods.

Paper Details

Date Published: 11 June 2014
PDF: 12 pages
J. Electron. Imaging. 23(3) 033012 doi: 10.1117/1.JEI.23.3.033012
Published in: Journal of Electronic Imaging Volume 23, Issue 3
Show Author Affiliations
Lei Yu, Chongqing Normal Univ. (China)
Yan Ma, Chongqing Normal Univ. (China)
Qi Cao, Logistical Engineering Univ. (China)


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