Human face detection using fast co-operative modular neural nets
Hazem Mokhtar El-Bakry · 2002
In this paper, a new approach to reduce the computation time taken by neural nets for the searching process is introduced. Both fast and co-operative modular neural nets are combined to enhance the performance of the detection process. Such an 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/spl times/20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/non-face images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such a division results in a reduction in the computational complexity, and thus a decrease in the time and memory needed during the testing of an image. Simulation results for the proposed algorithm show good performance. Also, a correction in the calculation for the speed-up ratio (for the object detection process) made by S. Ben-Yacoob (1997) is presented.