An efficient method for face feature extraction and recognition based on contourlet transforms and principal component analysis

In this paper, an efficient face recognition method based on the discrete contourlet transform using PCA and the Euclidean distance classifier is proposed. Each face is decomposed using the contourlet transform. The contourlet coefficients of low and high frequency in different scales and various angles are obtained. The frequency coefficients are used as a feature vector for further processing. PCA (Principal Component Analysis) is then used to reduce the dimensionality of the feature vector. Finally, the reduced feature vector is adopted as the face classifier. The test databases are projected onto contourlet-PCA subspace to retrieve reduced coefficients. These coefficients are used to match the feature vector coefficients of the training dataset using a Euclidean distance classifier. Experiments are carried out using the Face94 and IIT_Kanpur databases.