文章基本信息

标题：Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks
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作者：Thangairulappan Kathirvalavakumar ; Jeyasingh Jebakumari Beulah Vasanthi
期刊名称：Journal of Intelligent Learning Systems and Applications
印刷版ISSN：2150-8402
电子版ISSN：2150-8410
出版年度：2013
卷号：5
期号：2
页码：115-122
DOI：10.4236/jilsa.2013.52013
出版社：Scientific Research Publishing
摘要：An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.
关键词：Feature Extraction; Face Recognition; Wavelet Packets; Radial Basis Function Neural Network