Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/4012
Title: Facial Expression Recognition Using HMM with Observation Dependent Transition Matrix
Authors: Tsapatsoulis, Nicolas 
Leonidou, Miltiades
Kollias, Stefanos D. 
Keywords: Face recognition
Feature extraction
Filtering theory
Hidden Markov models
Issue Date: 1998
Publisher: IEEE
Source: IEEE Second Workshop on Multimedia Signal Processing, 1998, pages 89 - 95
Abstract: An expression recognition technique is proposed based on the hidden Markov models (HMM) ability to deal with time sequential data and to provide time scale invariability as well as a learning capability. A feature vector sequence is used for this purpose, which relies on optical flow extraction, as well as directional filtering of the motion field. Segmentation and identification of important facial parts are preceding feature extraction. The HMM is enhanced with an observation dependent transition matrix, being able to cope with the dynamics of emotions and the severe complexity of expressions timing. Experimental results are included illustrating the effectiveness of this method
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/4012
ISBN: 0-7803-4919-9
DOI: 10.1109/MMSP.1998.738918
Rights: © 1998, IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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