A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation | Zendy

Jian Zhao | Zendy; Huang Luxi | Zendy; Jian Jia | Zendy; Yu Xie | Zendy

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A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

Author(s) -

Jian Zhao,

Huang Luxi,

Jian Jia,

Yu Xie

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/683494

Subject(s) - facial recognition system , sparse approximation , computer science , face (sociological concept) , artificial intelligence , pattern recognition (psychology) , algorithm , process (computing) , representation (politics) , matching pursuit , compressed sensing , social science , sociology , politics , political science , law , operating system

A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm—avoiding to process useless information in the training dictionary. For the test samples that are affected by partial occlusion, corruption, and facial disguise, recognition rates of most algorithms fall rapidly. The robust version of this algorithm can identify these samples automatically and process them accordingly. The recognition rates on ORL database, Yale database, and FERET database are 95.5%, 93.87%, and 92.29%, respectively. The recognition performance under various levels of occlusion and corruption is also experimentally proved to be significantly enhanced

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