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Face recognition with histograms of fractional differential gradients

[+] Author Affiliations
Lei Yu

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

Yan Ma

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

J. Electron. Imaging. 23(3), 033012 (Jun 11, 2014). doi:10.1117/1.JEI.23.3.033012
History: Received January 22, 2014; Revised April 30, 2014; Accepted May 14, 2014
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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.

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Citation

Lei Yu ; Yan Ma and Qi Cao
"Face recognition with histograms of fractional differential gradients", J. Electron. Imaging. 23(3), 033012 (Jun 11, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.3.033012


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