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

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 Essex face 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.

Performance Evaluation of Face Recognition Based on the Fusion of Bit-Plane and Binary Image Compression Techniques Using Euclidean Distance Classifier

“…The SWT row vectors of both frontal and non-frontal reference images are concatenated to produce the final features. Kumar et al, [8] explained average wavelet packet coefficients and Radial Basis Function (RBF) neural network to recognize face. The RBF neural network is used to recognize the average wavelet packet coefficients.…”

“…From the related work of existing face recognition models [1, 2, 6, 7, 9-11, 20, 21], it is observed that the simple pixel level and average level approaches directly affect on the contrast of the image due to blurring effect. The spatial domain approach [8,10] does not have a fixed set of basis vectors hence, the spatial domain fusion produces spectral degradation which affects accuracy of the face model. The final fused images obtained by the transform domain results less spatial resolution which decreases the overall recognition rates.…”