Face recognition is an active research area in the artificial intelligence. A face recognition algorithm using the RBF network is proposed based on wavelet analysis and multi-level histogram sequence local Binary pattern (M-HSLBP). Since wavelet analysis is insensitive to changes in expression

it can express the principal features of the face image by compressing data. LBP is an efficient local texture description operator. The wavelet transformed images were scanned with multi-degree changeable Sub-windows. Sub-images were transformed by an enhanced LBP

and then the LBP features are concatenated into an enhanced feature vector

which can express both local and holistic features of the face image. RBF network with high generalization is a good classifier

especially for larger number of samples. Experimental results on ORL and YALE face show that the proposed algorithm

which achieves recognition accuracy of above 98% is more effective and faster than the traditional method.