FACE RECOGNITION USING GAUSSIAN MIXTURE MODEL & ARTIFICIAL NEURAL NETWORK

Face recognition is a non-contact and friendly biometric identification technology. It has broad application prospects in the military, public security and economic security. In this work, we also consider illumination variable database. The images have taken from far distance and do not consider the close view face of the individual as in most of the face databases, clear face view has been considered. In this first we located face as region of interest and then LBP and LPQ descriptors are used which is illuminance invariant in nature. After this GMM has been used to reduce feature set by taking negative log-likelihood from each LBP and LPQ descripted image histograms. After this ANN consumes stayed used for organization purposes. The investigational consequencesshow excellent correctness rates in overall testing of input data.