Low complexity 2-D Hidden Markov Model for face recognition

TitleLow complexity 2-D Hidden Markov Model for face recognition
Publication TypeConference Paper
Year of Publication2000
AuthorsOtluman, H., and T. Aboulnasr
Conference NameCircuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Pagination33 -36 vol.5
Keywords2-D discrete cosine transform compressed domain, 2-D representation, 8 pixel blocks scheme, computational complexity, discrete cosine transforms, face recognition, facial image statistical features, hidden Markov models, image representation, JPEG compatibility, low complexity 2-D hidden Markov model, modified Viterbi algorithm, nonoverlapped 8 times, Viterbi decoding

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 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


a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2020 The University of British Columbia