Dual Tree Complex Wavelet Transform based Face Recognition with Single View

In this paper, we propose a novel local appearance feature extraction method based on multi-resolution Dual Tree Complex Wavelet Transform (DT-CWT). Each face is described by a subset of band filtered images containing block-based DT-CWT coefficients. These coefficients characterize the face texture. The block based mean and variance of complex wavelet coefficients are used to describe the face image. The use of the complex wavelet transform is motivated by the fact that it helps eliminate the effects of non-uniform illumination, and the directional information provided by the different sub bands makes it possible to detect edge features with different directionalities in the corresponding image. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor waveletderived features and at the same time are of lower dimension when compared with that of Gabor wavelets. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the DT-CWT in block based approach performs well on illumination, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our proposed method has a really low computational complexity.