基于RBF神经网络的人脸识别研究  

Research on face recognition based on RBF neural networks

作　　者：吴畏[1] 肖南峰[1] 

机构地区：[1]华南理工大学计算机科学与工程学院

出　　处：《南京信息工程大学学报（自然科学版）》2010年第4期307-313,共7页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：国家自然科学基金与中国民用航空总局联合资助项目(60776816);广东省自然科学重点基金(251064101000005)

摘　　要：针对人脸识别技术中存在的高维问题、小样本问题和非线性问题展开研究.围绕人脸特征提取,采用基于主成分分析和Fisher线性鉴别来克服在人脸识别中的小样本问题,同时将人脸图像从高维空间映射到低维空间从而解决了高维问题;在分类识别方面,采用具有很强的非线性映射功能的RBF神经网络进行模式分类,能够解决人脸识别中的非线性问题.在ORL人脸数据库上进行的仿真实验表明,该方法进行人脸识别具有较高的识别率.In this paper,feature extraction and recognition of facial images is studied in order to resolve the highdimension problem,small size samples problem and non-linear separable problem that exist in face recognition technology.The proposed feature extract method based on Principal Component Analysis（PCA） and Fisher＇s Linear Discriminate（FLD） can solve the small size samples problem and the high-dimension problem by mapping the samples from a high-dimension space to a low-dimension Eigen space.In the recognition stage,RBF neural network,which represents a brilliant performance on small training set,non-linear separable and high-dimension pattern recognition problems,is used for pattern classification.Simulation results on the ORL face database indicate that the proposed method for face recognition yielded a good recognition rate.

关 键 词：人脸识别 特征提取 主成分分析 FISHER线性鉴别 RBF神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]