Abstract To overcome the limitations of traditional face recognition methods for single sample face recognition,a sub-pattern Gabor features fusion method for single sample face recognition is proposed. Firstly,facial local features are extracted by Gabor wavelet transformation. Then,the Gabor face images are blocked to take full advantage of the spatial location information of facial organs,and the minimum distance classifiers are used for each sub-pattern. Finally,the recognition result is achieved by the fusion of the sub-pattern classifiers′ results at the decision level. According to the difference of sub-pattern construction and fusion method,two kinds of sub-pattern Gabor features integration programs are proposed. The experimental results and comparative analysis on ORL face database and CAS-PEAL-R1 face database show that the proposed method achieves better classification rate and improves the performance of single sample face recognition system.
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2013, Vol. 26 
