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Face detection through neural network

The aim of this paper is to implement an effective system to locate upright frontal faces on monochromatic images with use of a neural network-based classifier. In this paper, a new approach to reduce the computation time taken by fast neural nets for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small size sub-images and then each one is tested separately using a fast neural network. Compared to conventional and fast neural networks, experimental results show that a speed up ratio is achieved when applying this technique to locate human faces in automatically in clustered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulted sub-images at the same time using the same number of fast neural networks. Moreover, the problem of sub-image centering and normalization in the Fourier space is solved.