Automatic face recognition using neural networks

Automatic recognition of individuals is a significant problem in the development of pattern recognition. In this paper, we introduce a simple technique for personal identification of human faces in cluttered scenes based on neural nets. In the detection phase, neural nets are used to test whether a window of 20×20 pixels contains a face or not. A major difficulty in the learning process comes from the large database required for face/nonface images. We solve this problem by dividing the data into two groups. Such division results in reduction of computational complexity, thus decreasing the time and memory needed during the image test. For the recognition phase, feature measurements are made through Fourier descriptors. Such features are modified to reduce the number of neurons in the hidden layer. Simulation results for the proposed algorithm show good performance compared with previous results.