Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks

T. Kathirvalavakumar, J. Vasanthi
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引用次数: 8

Abstract

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.
基于小波包系数和径向基函数神经网络的人脸识别
提出了一种基于平均小波包系数、紧凑而有意义的特征向量降维和径向基函数(RBF)神经网络识别的高效人脸识别系统。采用二维小波包变换对人脸图像进行分解。采用两种不同的方法对小波包变换得到的小波包系数进行平均。第一种方法是对一类样本的小波包系数取平均,然后进行分解。第二种方法是对一类样本的小波包系数取平均值。用RBF网络识别小波包平均系数。在olive - oracle Research Lab (ORL)、Japanese Female Facial Expression (JAFFE)和Essexface数据库三个人脸数据库上进行了测试。该方法具有降维、计算复杂度低、识别率高等优点。该方法降低了输入模式的维数,降低了计算复杂度。
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