文章基本信息

标题：NEURAL NETWORKS FOR FACE RECOGNITION USING SOM
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作者：Santaji Ghorpade ; Jayshree Ghorpade ; Shamla Mantri 等
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2010
卷号：1
期号：2
出版社：Ayushmaan Technologies
摘要：In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. Face recognition is one of the few biometric methods, which is very complicated system since the human faces change depending on their age, expressions etc. A human being has lots of expressions. So it is not possible to learn all types of expressions into the network. As a result, there is a solution for the unrecognized. Moreover due to the performance variations of the input device, face cannot be detected correctly and pattern may change extremely. In this paper we have developed and illustrated a recognition system for human faces using Kohonen self-organizing map (SOM). The main objective of our face recognition system was to obtain a model that is easy to learn i.e. minimization of learning time, react well with different facial expressions with noisy input and optimize the recognition as possible. Among the architectures and algorithms suggested for artificial neural network, the SOM has special property of effectively creating spatially organized “internal representation’ of various features of input signals and their abstractions. After supervised fine tuning of its weight vectors, the SOM has been particularly successful in various pattern recognition tasks involving very noisy signal. SOM are topologically ordered, which leads to good extracting feature ability. One develops realistic cortical structures when given approximations of visual environment as input, and is effective way to model the development of face recognition capability. The experimental result shows face recognition rate using SOM is 96.2% for 40 persons’ 400 images of AT&T database.
关键词：SOM(Self Organizing Mapping); PCA(Principal Component;Analysis); ICA(Independent Component Analysis); pattern;recognition; CN(Convolutional Network) and sampling.