Optimized sparse presentation-based classification method with weighted block and maximum likelihood model

Jun He; Tian Zuo; Bo Sun; Xuewen Wu; Chao Chen

doi:10.1117/12.2050374

13 June 2014 Optimized sparse presentation-based classification method with weighted block and maximum likelihood model

Jun He, Tian Zuo, Bo Sun, Xuewen Wu, Chao Chen

Proceedings Volume 9090, Automatic Target Recognition XXIV; 909003 (2014) https://doi.org/10.1117/12.2050374
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

This paper is aiming at applying sparse representation based classification (SRC) on face recognition with disguise or illumination variation. Having analyzed the characteristics of general object recognition and the principle of the classifier of SRC method, authors focus on evaluating blocks of a probe sample and propose an optimized SRC method based on position-preserving weighted block and maximum likelihood model. Principle and implementation of the proposed method have been introduced in the article, and experiments on Yale and AR face database have been given too. From experimental results, it can be seen that the proposed optimized SRC method works well than existing methods.

Citation Download Citation

Jun He, Tian Zuo, Bo Sun, Xuewen Wu, and Chao Chen "Optimized sparse presentation-based classification method with weighted block and maximum likelihood model", Proc. SPIE 9090, Automatic Target Recognition XXIV, 909003 (13 June 2014); https://doi.org/10.1117/12.2050374

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