Face recognition using the Wavelet tree and two-dimensional PCA
Lin Cao, Dengyi Chen, Kangning Du, Xian Zhu · 2012
Two-dimensional principal component analysis (2D-PCA) is a fast method for face recognition. The proposed method makes use of 2D-PCA based on two dimensional Wavelet tree matrices composed of the Wavelet approximation coefficients(WTMPCA) as opposed to the traditional 2D-PCA, which is grounded on 2D matrices in the image domain. By applying the three-level Wavelet decomposition, the new 2D matrix is made up of the approximation coefficients. The matrices in the Wavelet domain not only contain the whole information of the images, but also extract the local feature. Finally, the 2D-PCA is used under the new image matrix for face recognition. Experimental results on the ORL and a subset of CAS-PEAL face database show that WTMPCA method achieves 96% accuracy on face recognition using only one principal component vector.