Hybrid hidden Markov model for face recognition

TitleHybrid hidden Markov model for face recognition
Publication TypeConference Paper
Year of Publication2000
AuthorsOthman, H., and T. Aboulnasr
Conference NameImage Analysis and Interpretation, 2000. Proceedings. 4th IEEE Southwest Symposium
Pagination36 -40
Keywords2D module, candidate list, compressed domain, data compression, face recognition, hidden Markov models, HMM, hybrid hidden Markov model, local database search, low-complexity module, performance, query formulation, query processing, remote database search, visual databases

In this paper, we introduce a hybrid hidden Markov model (HMM) face recognition system. The proposed system contains a low-complexity 2D HMM-based face recognition (LC 2D-HMM FR) module that carries out a complete search in the compressed domain followed by a 1D HMM-based face recognition (1D-HMM FR) module which refines the search based on a candidate list provided by the first module. We also examine a remote database search methodology that may be helpful for accessing remote resources, where no prior information is assumed regarding the contents of the remote database. The performance of the hybrid HMM face recognition system is reported for both local and remote database search modes


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