A 2D Discrete Wavelet Transform Based 7-State Hidden Markov Model for Efficient Face Recognition

A novel Discrete Wavelet Transform (DWT) based on 7-States of Hidden Markov Model (HMM) for Face Recognition (FR) is proposed in this paper. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence vectors extraction. Extensive experiments have been conducted in our database and the FERET database shows that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available. As a novel point despite of five-state HMM used in pervious researches, we propose to use 7-state HMM to cover more specific details. A pre-processing procedure is introduced to reduce the complexity of the proposed system. It is evident from the outcome of these experiments that more information during training yield better results.