Two-Dimensional Canonical Correlation Analysis and Its Application to Face Recognition
Jingyu Yang · Journal of Changshu Institute of Technology · 2007
By analyzing the relativity between two-dimensional maximum scatter-difference discriminant analysis(2DMSLDA) and two-dimensional Fisher discriminant analysis(2DFLDA),according to traditional canonical correlation analysis(CCA),a novel method of combining different feature matrixes directly is proposed in this paper by using the main idea of image projection in face recognition.Compared with traditional CCA based on feature vectors,this method has the following two main advantages: first,the small sample size problem (SSS) occurred in traditional CCA is essentially inevitable as a result of the evidently reducing dimension of the covariance matrix.By the same reason,the second advantage is that much computational time would be saved if using the proposed method.Finally,extensive experiments performed on ORL face database verify the effectiveness of the proposed method.