Pseudo 2D hidden Markov models for face recognition using neural network coefficients

Face recognition is the preferred mode of identity recognition by humans from an image or video sequence: it is natural, robust and unintrusive. This work presents different Pseudo 2-D HMM structures for a face recognition showing performances reasonably fast for binary image. The proposed P2-D HMMs are made up of five levels of states, one for each Region of Interest (RoI) in which the input frontal images are sequenced: forehead, eyes, nose, mouth and chin. Each of P2-D HMMs has been trained by coefficients of an Artificial Neural Network used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. All the P2-D HMMs, applied to the validation set consisting of the Olivetti Research Laboratory (ORL) face database, have achieved good rates of recognition compared to other methods proposed in the literature and, in particular, the structure 3-6-6-6-3 has achieved a rate of recognition equal to 100%.