We describe a nonlinear joint transform correlator-based two-layer neural network that uses a supervised learning algorithm for real-time face recognition. The system is trained with a sequence of facial images and is able to classify an input face image in real time. Computer simulations and optical experimental results are presented. The processor can be manufactured into a compact low-cost optoelectronic system. The use of the nonlinear joint transform correlator provides good noise robustness and good image discrimination.
© 1995 Optical Society of America
Bahram Javidi, Jian Li, and Qing Tang, "Optical implementation of neural networks for face recognition by the use of nonlinear joint transform correlators," Appl. Opt. 34, 3950-3962 (1995)