Face Illumination and Occlusion based Experimental Research on Face Recognition using Artificial Neural Network

"Face Recognition System is traditional research and development in face detection and tracking are focused on video images or in still images. A face recognition system for classification and authentication using Genetic Algorithm and Optimization Soft Computing Techniques are proposed. The system is framed with three steps. Initially Image pre-processing methods are applied on the input image. Secondly, a neural based algorithm is presented, to detect frontal views of faces. The dimensionality of face image is reduced by the Principal Component Analysis (PCA) and the recognition is done by the Back propagation Neural Network (BPNN). These applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. Thirdly, Gabor feature extraction and feature selection using Genetic Algorithm (GA) is applied in the final step for recognizing the faces. The proposed approaches are tested on a number of face images. Experimental results demonstrate the higher extent performance of these algorithms. Here 200 face images from Yale database are taken and some performance metrics like acceptance ratio and execution time are calculated. Neural based face recognition is better performance of more than 90 % acceptance ratio. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. "