Maximum confidence hidden Markov modeling

Chih P. Liao*, Jen-Tzung Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper presents a compact and discriminative hidden Markov model (HMM) approach for general pattern classification. To achieve model compactness and discriminability, we simultaneously perform feature dimension reduction and HMM parameter estimation via maximizing the confidence of accepting the hypothesis that observations are from target HMM states rather than competing HMM states. A new discriminative training criterion is derived using hypothesis test theory. Particularly, we develop the maximum confidence hidden Markov modeling (MCHMM) framework for face recognition. Using this framework, we incorporate a transformation matrix to extract discriminative facial features. The continuous-density HMM parameters are estimated using the extracted features. Importantly, we adopt a consistent criterion to build whole framework including feature extraction and model estimation. From the experiments on ORL facial databases, we find that the proposed method obtains robust image segmentation performance in presence of different variations of facial expressions, orientations, etc. In comparison of previous HMM approaches, the proposed MCHMM achieves better recognition accuracies and image segmentation.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
DOIs
StatePublished - 1 Dec 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period14/05/0619/05/06

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    Liao, C. P., & Chien, J-T. (2006). Maximum confidence hidden Markov modeling. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings [1661334] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 5). https://doi.org/10.1109/ICASSP.2006.1661334