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Journal of Electronic Imaging

Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions
Author(s): Yong Cheng; Zuoyong Li; Liangbao Jiao; Hong Lu; Xuehong Cao
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Paper 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.

Paper Details

Date Published: 26 August 2016
PDF: 11 pages
J. Electron. Imag. 25(4) 043028 doi: 10.1117/1.JEI.25.4.043028
Published in: Journal of Electronic Imaging Volume 25, Issue 4
Show Author Affiliations
Yong Cheng, Nanjing Institute of Technology (China)
Southeast Univ. (China)
Zuoyong Li, Fujian Provincial Key Lab. of Information Processing and Intelligent Control (China)
Liangbao Jiao, Nanjing Institute of Technology (China)
Hong Lu, Nanjing Institute of Technology (China)
Xuehong Cao, Nanjing Institute of Technology (China)

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