Face detection using fast neural networks and image decomposition

Abstract

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 in 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 automatically in cluttered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulting 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.