Face Recognition Based on Coarse Sub-Bands of Contourlet Transformation and Principal Component Analysis

In this paper, a face recognition system is implemented by using contourlet transformation (ct) as a two dimensional transformation defined in discrete form and principal component analysis (pca) as a subspace method to form the feature vectors, is implemented. every input image is decomposed by ct up to three levels and the ct coefficients are obtained at three scales and 15 orientations. the obtained ct coefficients are used by pca to form the feature vectors. at the end, the euclidean distance is used for classification. our experimental results on orl data base show the appropriate performance in comparison with other approaches; even though for each subject only one image is used for training and other 9 images are used for testing. the average accuracy of our proposed algorithm for face recognition is 96.07%.