Performance improvement for 2-D face recognition using multi-classifier and BPN

Human face recognition and detection has become a very interesting field for the researcher and this interest is motivated by the huge demand of extensive applications of the real time surveillance system and the static matching system like DMV licenses, port authority and bank system. The image processing, neural network and computer vision are the most area active research areas. Many of papers and approaches have been introduced in the past decades. It is difficult to create and design an efficient computational system for accurate human face recognition because of the complex visual of the human face, which changes dramatically based on the variant effects. In this paper, we propose a new framework for 2D face recognition using five different distance algorithms and Backpropagation Neural network (BPN) to improve conventional Principal Component Analysis (PCA) approach. The performance of the new framework is compared with the performance of the K Nearest-Neighbor (KNN) classifier using five different distance algorithms then we combine them using the least square root algorithm to achieve a higher accuracy. Our experimental results on AT&T (formerly ORL) and Yale face databases show that our proposed method improves the overall performance of face recognition system with respect to existing approaches. The results show the high accuracy can be achieved by using %15 of the image features which increases the recognition performance rate with less computational cost than the existing methods.