Javidi B, Li J, Tang Q
Appl Opt. 1995 Jul 10;34(20):3950-62. doi: 10.1364/AO.34.003950.
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.
我们描述了一种基于非线性联合变换相关器的两层神经网络，该网络使用监督学习算法进行实时人脸识别。该系统通过一系列面部图像进行训练，能够实时对输入的面部图像进行分类。文中给出了计算机模拟和光学实验结果。该处理器可以制造成紧凑的低成本光电系统。非线性联合变换相关器的使用提供了良好的噪声鲁棒性和良好的图像辨别能力。
