Face recognition using neural networks with multiple combinations of categories
Takashi Yahagi, Hiroaki Takano · Systems and Computers in Japan · 1995
Abstract Neural networks trained via backpropagation are now widely applied in a pattern recognition method. However, since it becomes much more difficult for a network to accomplish its task when the number of categories increases, research on multiple network combination is active. Novel learning and recognition processes are proposed here. It is shown that by combining small‐scale neural networks, the proposed method allows exploitation of the potential capabilities of the networks. In the learning process, multiple networks are trained with patterns organized in overlapping groups. During the recognition process, response is obtained by making the networks compete with each other. In experiments involving recognition of individuals from various facial images and different expressions, a recognition rate of higher than 96 percent was obtained for 20 individuals and 131 images. Furthermore, results of simulations in which noise was added confirmed that the proposed method is robust with respect to pattern changes.