Face recognition based on wavelet transform, two-dimensional principal component analysis and independent component analysis

Combined with wavelet transform (WT), two-dimensional principal component analysis (2DPCA) and independent component analysis (ICA), a method for face recognition is presented. Firstly, the original images are decomposed into high-frequency and low-frequency components by using WT. The horizontal and vertical high-frequency components are ignored, and the noise is eliminated. Then, dimension reduction is performed by 2 DPCA, and a whitened matrix is obtained. The independent components of training samples are acquired by ICA. Meanwhile, an independent basis subspace is constructed by the independent basis of training samples. Finally, the projected features of training and the testing samples on the independent basis subspace are gained, therefore face recognition can be realized according to the nearest neighbour rule. Experimental results on Olivetti Research Laboratory (ORL) and Yale face database show that the recognition rate by the proposed method is higher than that by 2DPCA, 2DPCA-ICA, and WT-2DPCA respectively.