A new technique for Face Recognition using 2D-Gabor Wavelet Transform with 2D-Hidden Markov Model approach

A Discrete Gabor Wavelet Transform (DGWT) based 2D Hidden Markov Model (2DHMM) approach for Face Recognition (FR) is proposed in this paper. To improve the accuracy of the face recognition algorithm, a Gabor Wavelet Transform is used in obtaining the observation sequence vectors. We have conducted extensive experiments ORL database which shows that the proposed method can improve the accuracy significantly, especially when the face image dataset is large with limited training images. Unlike the pervious HMMs used for FR, we propose 2D HMM with Expectation-Maximization (EM)algorithm suitable for almost perfect estimation as feature vectors. This model of 2D HMM shows superior image segmentation for learning process. A recognition rate of 99% is achieved.

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