Hybrid approaches to frontal view face recognition using the neural network

In this paper, for frontal view face recognition hybrid approaches using neural networks (NNs) and hidden Markov models (HMMs) are proposed. In the preprocessing stage, edges of a face are detected using the conventional locally adaptive threshold (LAT) scheme and facial features are extracted based on generic knowledge of facial components. In constructing a database with normalized features, we employ HMM parameters of each person computed by the forward-backward algorithm. Computer simulation shows that the proposed HMM-NN algorithm yields higher recognition rate compared with several conventional face recognition algorithms