2DICA Based on Wavelet Transformation and Applications in Face Recognition
Chunzhi Li · Jisuanji fangzhen · 2007
Combined with the traits of Two-Dimensional Principal Component Analysis (2DPCA) and Independent Component Analysis (ICA), the concept of Two-Dimensional Independent Component Analysis (2DICA) is presented in this paper. Firstly, dimension reduction is done to the preprocessed face images by way of 2DPCA, and the whitened matrix is obtained. Then, the independent components of face images are acquired by way of ICA. Finally, independent basis subspace is constructed by independent basis of face images, thereby face recognition can be fulfilled according to the projected features of testing samples on the independent basis subspace. In this paper,a novel method for Two-Dimensional Independent Component Analysis based on Wavelet-Transform (WT-2DICA) in face recognition is presented. Firstly, original images are decomposed into high-frequency and low-frequency components with the help of Wavelet Transform (WT), and high-frequency components are ignored, so the prime features of original images can be attained. Secondly, the projected features are solved by 2DICA. Finally, face recognition can be realized according to the nearest neighbour rule. Experimental results on ORL (Olivetti Research Laboratory) and Yale face database show that correct recognition rate by WT-2DICA is higher than that by 2DPCA and 2DICA respectively, and the method in this paper valid in face recognition.