OPTICAL IMPLEMENTATION OF NEURAL NETWORKS FOR FACE RECOGNITION BY THE USE OF NONLINEAR JOINT TRANSFORM CORRELATORS
OPTICAL IMPLEMENTATION OF NEURAL NETWORKS FOR FACE RECOGNITION BY THE USE OF NONLINEAR JOINT TRANSFORM CORRELATORS
复制标题
DOI：
10.1364/ao.34.003950
复制
发表时间：
1995-07-10
期刊：
影响因子：
1.9
通讯作者：
TANG, Q
中科院分区：
文献类型：
--
作者：
JAVIDI, B;LI, J;TANG, Q
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，它使用了一种监督学习算法来进行实时人脸识别。该系统使用人脸图像序列进行训练，并能够对输入的人脸图像进行实时分类。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、低成本的光电系统。非线性联合变换相关器的使用提供了良好的噪声稳健性和良好的图像识别率。
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.