In this paper, a new approach to reduce the computation time taken by neural networks for the searching process is introduced. Both fast and cooperative modular neural networks 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 networks are used to test whether a window of 20x20 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 networks is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm on Bio database mixed with other images show a good performance.
['Hazem M. El-Bakry', 'Qiangfu Zhao']
Fast co-operative modular neural processors for human face detection