Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks - Journal of Intelligent Learning Systems and Applications

JILSA > Vol.5 No.2, May 2013

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

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Download as PDF (Size: 306KB) PP. 115-122

DOI: 10.4236/jilsa.2013.52013 3,263 Downloads 5,653 Views Citations

Author(s)

Thangairulappan Kathirvalavakumar, Jeyasingh Jebakumari Beulah Vasanthi

Affiliation(s)

Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, India.
Department of Computer Science, Virudhunagar Hindu Nadars’ Senthikumara Nadar College, Virudhunagar, India.

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.

KEYWORDS

Feature Extraction; Face Recognition; Wavelet Packets; Radial Basis Function Neural Network

Share and Cite:

T. Kathirvalavakumar and J. Vasanthi, "Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 2, 2013, pp. 115-122. doi: 10.4236/jilsa.2013.52013.

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