11 June 2014 Face recognition with histograms of fractional differential gradients
Author Affiliations +
J. of Electronic Imaging, 23(3), 033012 (2014). doi:10.1117/1.JEI.23.3.033012
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.
Lei Yu, Yan Ma, Qi Cao, "Face recognition with histograms of fractional differential gradients," Journal of Electronic Imaging 23(3), 033012 (11 June 2014). https://doi.org/10.1117/1.JEI.23.3.033012


Facial recognition systems

Principal component analysis

Feature extraction

Autoregressive models

Binary data


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