Referenced Items are not available for this document.
The embedded hidden Markov model (HMM) is a statistical model that can be used in many pattern recognition and computer vision applications. This model inherits the partial size invariance of the standard HMM, and, due to its pseudo two-dimensional structure, is able to model two-dimensional data such as images, better than the standard HMM. We describe the maximum likelihood training for the continuous mixture embedded HMM and present the performance of this model for face detection and recognition. The experimental results are compared with other approaches to face detection and recognition
Published in:
Image Processing, 2000. Proceedings. 2000 International Conference on
(Volume:1
)
Date of Conference: 2000
- Page(s):
- 33 - 36 vol.1
- Meeting Date :
- 10 Sep 2000-13 Sep 2000
- ISSN :
- 1522-4880
- Print ISBN:
- 0-7803-6297-7
- INSPEC Accession Number:
- 6983798
- Conference Location :
- Vancouver, BC
- Digital Object Identifier :
- 10.1109/ICIP.2000.900885
- Product Type:
- Conference Publications
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