Efficient face recognition using wavelet-based generalized neural network

This paper presents an efficient face recognition method where enhanced local Gabor binary pattern histogram sequence has been used for efficient face feature extraction and generalized neural network with wavelet as activation function is being used for classification. In this method the face is first decomposed into multiresolution Gabor wavelets the magnitude responses of which are applied to enhanced local binary patterns. The efficiency has been significantly improved by combining two efficient local appearance descriptors named Gabor wavelet and enhanced local binary pattern with generalized neural network. Generalized neural network is a proven technique for pattern recognition and is insensitive to small changes in input data. The proposed method is robust-to-slight variation of imaging conditions and pose variations. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate, equal error rate and time complexity.

► Decomposition of image using Gabor wavelets.
► Apply local binary pattern on image to get approximate subbands.
► Concatenate two initial moments from each subband for feature vector generation.
► Use of generalized mean neural network based classifier for improved face recognition.
► Validation through experiments on various face databases having pose and illumination.