Face recognition by RBF with wavelet, DCV and modified LBP operator face representation methods

Abstract

A face recognition system must be robust with respect to many variability such as viewpoint, illumination, and facial expression of the face image. The main aim of the proposed work is to represent and recognize face images with different poses. An efficient face recognition system with face image representation using wavelet and averaged wavelet packet coefficients in the form of Discriminative Common Vector (DCV) and modified Local Binary Patterns (LBP) and recognition using radial basis function (RBF) neural network is presented. Face images are decomposed by 2-level two-dimensional (2-D) wavelet and wavelet packet transformation. The discriminative common vectors are obtained for wavelet and averaged wavelet packet coefficients. Newly proposed LBP operator is applied on the DCV and LBPs are obtained. Histogram values are generated for the LBP and recognized using RBF network. The proposed work is tested on three standard face databases namely Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The extracted features are recognized by the proposed method results in good recognition rates. The execution time for the proposed methods is also less because of the meaningful extracted features obtained from the face representation methods.