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Face recognition using spatially smoothed discriminant structure-preserved projections

[+] Author Affiliations
Yugen Yi

Northeast Normal University, College of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, School of Mathematics and Statistics, Changchun 130117, China

Wei Zhou

Northeast Normal University, College of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, Key Laboratory of Intelligent Information Processing of Jilin Universities, Changchun 130117, China

Jianzhong Wang

Northeast Normal University, College of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, National Engineering Laboratory for Druggable Gene and Protein Screening, Changchun 130117, China

Yanjiao Shi

Northeast Normal University, College of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, School of Mathematics and Statistics, Changchun 130117, China

Jun Kong

Northeast Normal University, College of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, Key Laboratory of Intelligent Information Processing of Jilin Universities, Changchun 130117, China

J. Electron. Imaging. 23(2), 023012 (Mar 31, 2014). doi:10.1117/1.JEI.23.2.023012
History: Received August 12, 2013; Revised February 16, 2014; Accepted February 20, 2014
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Abstract.  Recently, structure-preserved projections (SPP) were proposed as a local matching-based algorithm for face recognition. Compared with other methods, the main advantage of SPP is that it can preserve the configural structure of subpatterns in each face image. However, the SPP algorithm ignores the information among samples from different classes, which may weaken its recognition performances. Moreover, the relationships of nearby pixels in the subpattern are also neglected in SPP. In order to address these limitations, a new algorithm termed spatially smoothed discriminant structure-preserved projections (SS-DSPP) is proposed. SS-DSPP takes advantage of the class information to characterize the discrimination structure of subpatterns from different classes, and a new spatially smooth constraint is also derived to preserve the intrinsic two-dimensional structure of each subpattern. The feasibility and effectiveness of the proposed algorithm are evaluated on four standard face databases (Yale, extended YaleB, CMU PIE, and AR). Experimental results demonstrate that our SS-DSPP outperforms the original SPP and several state-of-the-art algorithms.

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© 2014 SPIE and IS&T

Citation

Yugen Yi ; Wei Zhou ; Jianzhong Wang ; Yanjiao Shi and Jun Kong
"Face recognition using spatially smoothed discriminant structure-preserved projections", J. Electron. Imaging. 23(2), 023012 (Mar 31, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.2.023012


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