Fast cooperative modular neural nets for human face detection

A new approach to reduce the computation time taken by neural nets for the searching process is introduced. Both fast and cooperative modular neural nets are combined to enhance the performance of the detection process. Such approach is applied to identify human faces automatically in cluttered scenes. In the detection phase, neural nets are used to test whether a window of 20×20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreases the time and memory needed during the test of an image. Simulation results for the proposed algorithm show good performance. Also, a correction in calculation for the speed up ratio (for object detection process) in another paper is presented (see S. Ben-Yacoub, "Fast Object Detection using MLP and FFT", IDIAP-RR 11, IDIAP, (1997))