Extended CRC: Face Recognition with a Single Training Image per Person via Intraclass Variant Dictionary

Guojun LIN  Mei XIE  Ling MAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.10   pp.2290-2293
Publication Date: 2013/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2290
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
Keyword: 
face recognition,  sparse representation,  collaborative representation,  single training image,  

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Summary: 
For face recognition with a single training image per person, Collaborative Representation based Classification (CRC) has significantly less complexity than Extended Sparse Representation based Classification (ESRC). However, CRC gets lower recognition rates than ESRC. In order to combine the advantages of CRC and ESRC, we propose Extended Collaborative Representation based Classification (ECRC) for face recognition with a single training image per person. ECRC constructs an auxiliary intraclass variant dictionary to represent the possible variation between the testing and training images. Experimental results show that ECRC outperforms the compared methods in terms of both high recognition rates and low computation complexity.