Low complexity 2-D Hidden Markov Model for face recognition
H. Otluman, T. Aboulnasr · 2002
In this paper, a low complexity 2-D Hidden Markov Model (HMM) Face Recognition (FR) system is introduced to provide a 2-D representation of the statistical features of the facial image, as opposed to the 1-D HMM and the 2-D Pseudo HMM (2-DPHMM) found in the literature. The proposed model is designed to have low complexity when compared to the Markov Random Field based 2-D HMM (MRF 2-D HMM). The model is implemented in the 2-D Discrete Cosine Transform (DCT) compressed domain based on a non-overlapped 8/spl times/8 pixel blocks scheme to maintain the compatibility with JPEG. It is shown that the proposed model has considerably lower complexity than the MRF 2-D KMM and 2-D PHMM.