液晶与显示2017，Vol.32Issue(3)：213-218,6.DOI:10.3788/YJYXS20173203.0213
改进非负矩阵分解的神经网络人脸识别
Face recognition based on improved NMF and neural network
摘要
Abstract
In order to promote the accuracy of facial recognition, an improved algorithm of the neural network based on NMF for recognizing faces was proposed.First, features from facial images are extracted by using the improved method of non-negative matrix factorization to increase the decomposition speed of NMF.Then the extracted feature information is made as a neural network learning entrance for characteristics training.Due to the problems of minimum local value and slow convergence speed in the process of neural network learning, an improved genetic algorithm was used to optimize the neural network, and final facial recognition result was obtained.Experimental results indicate that the facial recognition method using the improved NMF can reduce the classification training load and operand of the neural network, and can also increase the facial recognition rate.Compared with other methods, our method has a higher facial recognition rate.The proposed method has high facial feature decomposition speed, it also promote the accuracy of neural network training and improve the convergence speed to make the rate of facial recognition higher.With more than 40 number of feature vectors, the facial recognition accuracy can basically keep more than 95%.关键词
机器视觉/人脸识别/非负矩阵分解/遗传算法/神经网络Key words
machine vision/face recognition/nonnegative matrix factorization/genetic algorithm/neural network分类
信息技术与安全科学引用本文复制引用
郑明秋,杨帆..改进非负矩阵分解的神经网络人脸识别[J].液晶与显示,2017,32(3):213-218,6.基金项目
吉林省科技发展计划项目(No.20130303011GX,No.20140204050GX) Supported by Project Agreement for Science & Technology Development of Jilin Province(No.20130303011GX,No.20140204050GX) （No.20130303011GX,No.20140204050GX）
