Face Representation Using Averaged Wavelet, Micro Patterns and Recognition Using RBF Network
Recognition of human faces is a very important task in many applications such as authentication and surveillance. An efficient face recognition system with face image representation using averaged wavelet and wavelet packet coefficients, Discriminative Common Vector (DCV) and modified Local Binary Patterns (LBP) and recognition using radial basis function (RBF) network is presented. Face images are decomposed by 2-level wavelet and wavelet packet transformation. The discriminative common vectors are obtained for averaged wavelet. The new proposed LBP operator is applied on the obtained DCV and also applied on averaged wavelet packet coefficients of all the samples of a class. The histogram values obtained from the LBP are recognized using RBF network. The proposed work is tested on three face databases such as Olivetti Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The proposed method results in good recognition rates along with less training time because of the extracted discriminant input from the preprocessing steps involved in the proposed work.
KeywordsFace recognition Wavelet Wavelet packets Discriminative common vector Classification Local binary patterns Radial basis function network
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- 7.Feng, G.C., Yuen, P.C., Dai, D.Q.: Human face recognition using PCA on wavelet subband. J. Electron. Imaging 9, 226–233 (2001)Google Scholar
- 10.Kathirvalavakumar, T., Vasanthi, J.J.B.: Face representation using Wavelet, DCV and Modified Local Binary Patterns and Recognition by RBF. Journal of Machine Learning and Cybernetics (2013)Google Scholar