Face Recognition Using Dual Tree Complex Wavelet Transform

Abstract

We propose a novel face recognition using Dual Tree Complex Wavelet Transform (DTCWT), which is used to extract features from face images. The Complex Wavelet Transform is a tool that uses a dual tree of wavelet filters to find the real and imaginary parts of complex wavelet coefficients. The DT-CWT is, however, less redundant and computationally efficient. CWT is a relatively recent enhancement to the discrete wavelet transform (DWT). We show that it is a well-suited basis for this problem as it is directionally selective, smoothly shift invariant, optimally decimated at coarse scales and invertible (no loss of information). Our face recognition scheme is fast because of the decimated nature of the DTCWT. Dual Tree methods are based on image at different resolution. Normalization is done to reduce dimensionality which will reduce memory problem and computation time. Here Principal Component Analysis which is a linear dimensionality reduction technique, that attempt to represent data in lower dimensions, is used to perform the face recognition. PCA is applied that deals with the decomposition of the training set into the Eigenvectors called Eigen faces. Various discrimination analyzes such as, Euclidean, L1, L2 and Cosine similarity are used for the recognition of face images.