Title
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
Abstract
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).
Year
DOI
Venue
2002
10.1109/ICIP.2002.1038175
Image Processing. 2002. Proceedings. 2002 International Conference  
Keywords
Field
DocType
eigenvalues and eigenfunctions,face recognition,multilayer perceptrons,pattern classification,principal component analysis,Honglk university databases,Olivetti databases,artificial neural networks,bond rating classification,eigenpaxel method,face invariant classifier,judge analyzer,multi-layer perceptron neural network,parallel face classifier,pattern recognition problems,principal component analysis,recognition rates
Kernel (linear algebra),Facial recognition system,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Face detection,Artificial neural network,Classifier (linguistics),Perceptron,Machine learning,Principal component analysis
Conference
Volume
ISSN
Citations 
1
1522-4880
1
PageRank 
References 
Authors
0.37
2
4
Name
Order
Citations
PageRank
Peter V. Bazanov110.37
Tae-Kyun Kim21987129.30
Seok-cheol Kee312913.94
Sang Uk Lee41879180.39