N-feature neural network human face recognition

摘要

This paper introduces an efficient method for human face recognition system, which is called the hybrid N-feature neural network (HNFNN) human face recognition system. The HNFNN employs a set of different kind of features from face images with radial basis function (RBF) neural networks, which are fused together through the majority rule. The proposed method improves the performance of the system by combining RBF neural networks, training with different learning algorithms, in committees. This article also evaluates how the performance can be improved by disregarding irrelevant data from the face images by defining the efficient parameters. Experimental results on the ORL and Yale face databases confirm that the proposed method lends itself to higher classification accuracy relative to existing techniques.