Multi-Angle Face Detection Using Back Propagation Neural Network
In machine vision application, the main part to analyze an image is to identify its features which contribute to efficiency of the system. Many applications in vision system and image analysis used face detection as a feature of their whole system development. In application such as video surveillance, fatigue detection and security system, face is a fundamental step in the analysis before proceed to system implementation. It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. In this paper, we propose a neural network based approach to identify multi-angle face which falls into five categories: all left-side face, half left-side face, positive face, half right-side face, and all right-side face. More than 100 images of each category have been used for training and testing of face detection and its features was extracted to be an input to BP neural network. We analyzed the result of training and testing set of neural network and the best classification achieved was 90.7%.
Dongye Sun, Wen-Pei Sung and Ran Chen
K. H. B. Ghazali et al., "Multi-Angle Face Detection Using Back Propagation Neural Network", Applied Mechanics and Materials, Vols. 121-126, pp. 2411-2415, 2012