DocumentCode :
3500315
Title :
Multi-scale primal feature based facial expression modeling and identification
Author :
Yin, Lijun ; Wei, Xiaozhou
Author_Institution :
Dept. of Comput. Sci., New York State Univ., Binghamton, NY
fYear :
2006
fDate :
2-6 April 2006
Firstpage :
603
Lastpage :
608
Abstract :
In this paper, we present our newly developed face expression modeling system for expression analysis and identification. Given a face image at a front view, a realistic facial model is created using our extended topographic analysis and model instantiation approach. Our facial expression modeling system consists of two major components: (1) facial feature representation using the coarse-to-fine multiscale topographic primitive features and (2) an adaptive generic model individualization process based on the primal facial surface feature context. The algorithms have been tested using both static images and facial expression sequences. The usefulness of the generated expression models is validated by our 3D facial expression analysis algorithm. The accuracy of the generated expression model is evaluated by the comparison between the generated models and the range models obtained by a 3D digitizer
Keywords :
face recognition; feature extraction; image representation; image sequences; 3D digitizer; adaptive generic model individualization process; coarse-to-fine multiscale topographic primitive features; face expression modeling system; facial expression identification; facial expression sequences; facial feature representation; multiscale primal face feature; primal facial surface feature context; Adaptation model; Algorithm design and analysis; Computer science; Context modeling; Face recognition; Facial features; Image analysis; Shape; Surface topography; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on
Conference_Location :
Southampton
Print_ISBN :
0-7695-2503-2
Type :
conf
DOI :
10.1109/FGR.2006.80
Filename :
1613085
Link To Document :
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