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Improved similarity measure-based graph embedding for face recognition

[+] Author Affiliations
Yongxin Ge

Chongqing University, School of Software Engineering, Chongqing, China 400044

Dan Yang

Chongqing University, School of Software Engineering, Chongqing, China 400044

Xiaohong Zhang

Chongqing University, School of Software Engineering, Chongqing, China 400044

Jiwen Lu

Advanced Digital Sciences Center, Singapore, 138632

J. Electron. Imaging. 21(1), 013002 (Feb 22, 2012). doi:10.1117/1.JEI.21.1.013002
History: Received December 28, 2010; Revised November 27, 2011; Accepted December 21, 2011
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Abstract.  We propose an improved similarity measure (ISM) and apply it to the existing graph embedding (GE) framework to derive a new improved similarity measure-based graph embedding (ISM-GE) method for face recognition. Our work is motivated by the fact that both the Euclidean metric and the correlation metric are useful and effective for characterizing the similarity of face samples, and we combine these two metrics to form a new ISM to measure the similarity of face samples. We further utilize the proposed ISM in the existing GE framework and develop a new ISM-GE method for face feature extraction and recognition. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.

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Citation

Yongxin Ge ; Dan Yang ; Xiaohong Zhang and Jiwen Lu
"Improved similarity measure-based graph embedding for face recognition", J. Electron. Imaging. 21(1), 013002 (Feb 22, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.1.013002


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