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

[+] Author Affiliations
Yong Cheng

Nanjing Institute of Technology, School of Communication Engineering, No. 1, Hongjing, Nanjing 211167, China

Southeast University, School of Automation, No. 2, Sipailou, Nanjing 210096, China

Zuoyong Li

Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, No. 1, Wenxian Road, Fuzhou 350121, China

Liangbao Jiao, Hong Lu, Xuehong Cao

Nanjing Institute of Technology, School of Communication Engineering, No. 1, Hongjing, Nanjing 211167, China

J. Electron. Imaging. 25(4), 043028 (Aug 26, 2016). doi:10.1117/1.JEI.25.4.043028
History: Received February 5, 2016; Accepted August 8, 2016
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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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Citation

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", J. Electron. Imaging. 25(4), 043028 (Aug 26, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.4.043028


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