Kernel Canonical Correlation Analysis with Sparse Representation for Facial Expression Recognition
ZHOU Xiao-Yan1,2 ,ZHENG Wen-Ming3,XIN Ming-Hai3
1. Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science Technology,Nanjing 210044 2.Jiangsu Technology Engineering Center of Meteorological Sensor Network,Nanjing University of Information Science Technology,Nanjing 210044 3.Research Center for Learning Science,Southeast University,Nanjing 210096
Abstract：In facial expression recognition,the existences of image noises and the irrelevant image information to the expression changes usually influence the recognition accuracy. The traditional facial expression recognition method using kernel canonical correlation analysis (KCCA) is difficulty to solve this problem. To overcome this drawback,a kernel canonical correlation analysis with sparse representation (SKCCA) is proposed and applied to the facial expression recognition. The basic idea of the SKCCA method is to utilize the sparse representation approach to choose the spectral components of the facial feature matrix before modeling the correlation between facial feature matrix and the expression semantic feature matrix. Then,the expression recognition is carried out based on the correlation model. To demonstrate the superiority of the proposed method over the traditional KCCA method,extensive experiments are conducted on the JAFFE database and the experimental results confirm the effectiveness of the proposed method.
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2013, Vol. 26 
