Multi-Features and Multi-Stages RBF Neural Network Classifier with Fuzzy Integral in Human Face Recognition

摘要

This paper presents a high accuracy human face recognition system using multi-feature extractors and multi-stages classifiers (MFMC), which are fused together through fuzzy integral. The classifiers used in this paper are Radial Basis Function (RBF) neural network while feature vectors are generated by applying PZM, PCA and DCT to the face images separately. Each of the feature vectors are sent to an RBF neural network classifiers and the output of these classifiers are fused to obtain better recognition rate. Experimental results on the ORL and Yale database yield excellent recognition rate.