Sciweavers

Share
ICIP
2002
IEEE

Hybrid and parallel face classifier based on artificial neural networks and principal component analysis

10 years 2 months ago
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
We present a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is extracted by using the eigenpaxel method, which is based on principal component analysis (PCA) of a group of pixel, that is called a paxel. To classify subjects, multi-layer perceptron neural network (NN)s are trained for each eigenpaxel. These parallel NN kernels provide sage, fast and efficient classification. To combine the results of parallel NNs, a novel judge analyzer is proposed based on bond rating classification and prediction. The proposed judge strategy can detect distinguishable face features even in arguable situations. The proposed method was evaluated on Olivetti and HongIk university (HIU) face databases and it yields that a top recognition rates are 95.5% and 94.11% respectively, which are better results than the previous eigenpaxel and NN approach [1].
Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sa
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2002
Where ICIP
Authors Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sang Uk Lee
Comments (0)
books