Complex Wavelet-Based Face Recognition Using Independent Component Analysis

In this paper, a novel face recognition method is proposed by exploiting the dual-tree complex wavelet transform (DT-CWT) and independent component analysis (ICA). The DT-CWT is applied to face images to provide a better representation for feature extraction. The dimension of the DT-CWT-represented feature vectors is further reduced by the principal component analysis (PCA). The ICA is then exploited to reduce the feature redundancies and derive the independent feature vectors for probabilistic reasoning model (PRM) classifier. Extensive experimental results have demonstrated that the proposed method has consistently yielded the best face recognition performance conducted on the ORL database.