Human face recognition using neural networks

Automatic recognition of human faces is a significant problem in the development and application of pattern recognition. We introduce a simple technique for 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/spl times/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 these data into two groups. Such a division results in reduction of computational complexity and thus decreasing the time and memory needed during the testing of an image. For the recognition phase, feature measurements are made through Fourier descriptors. Such features are used as input to the neural classifier for training and recognition of ten human faces. Simulation results for the proposed algorithm show a good performance during testing.