基于径向基函数神经网络的人脸识别

Weihua Wang
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引用次数: 13

摘要

人脸识别是计算机模式识别领域的一个活跃课题,由于其具有广泛的应用前景,近几十年来一直是人们关注的焦点。提出了一种基于RBF神经网络的人脸识别方法。讨论了人脸图像向量的特征问题、图像大小的归一化问题和隐层神经节点的训练算法问题。在ORL人脸数据库上进行了实验。结果表明,与BP神经网络相比,RBF神经网络可以有效地降低错误率、训练时间和识别时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Recognition Based on Radial Basis Function Neural Networks
The face recognition is an active subject in the area of computer pattern recognition, which has been a focus in reach for the last couple of decades because of its widely potential applications. A face recognition approach is put forward based on the RBF neural network. Also discussed are the problem of feature of a face image vector, the problem of normalization of the image-size, and the problem of training algorithm of hidden layerpsilas neural nodes. Experiments have been conducted on ORL face database. The results show that compared with BP neural network, the RBF neural network can decrease the error rate, the training time, and the recognition time efficiently.
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