Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions

Yong Cheng; Zuoyong Li; Liangbao Jiao; Hong Lu; Xuehong Cao

doi:10.1117/1.JEI.25.4.043028

26 August 2016 Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions

Yong Cheng, Zuoyong Li, Liangbao Jiao, Hong Lu, Xuehong Cao

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043028 (August 2016). https://doi.org/10.1117/1.JEI.25.4.043028

Abstract

We improved classic retinal modeling to alleviate the adverse effect of complex illumination on face recognition and extracted robust image features. Our improvements on classic retinal modeling included three aspects. First, a combined filtering scheme was applied to simulate functions of horizontal and amacrine cells for accurate local illumination estimation. Second, we developed an optimal threshold method for illumination classification. Finally, we proposed an adaptive factor acquisition model based on the arctangent function. Experimental results on the combined Yale B; the Carnegie Mellon University poses, illumination, and expression; and the Labeled Face Parts in the Wild databases show that the proposed method can effectively alleviate illumination difference of images under complex illumination conditions, which is helpful for improving the accuracy of face recognition and that of facial feature point detection.

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Yong Cheng, Zuoyong Li, Liangbao Jiao, Hong Lu, and Xuehong Cao "Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions," Journal of Electronic Imaging 25(4), 043028 (26 August 2016). https://doi.org/10.1117/1.JEI.25.4.043028

Published: 26 August 2016

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