DocumentCode :
2117866
Title :
3D facial expression recognition based on automatically selected features
Author :
Tang, Hao ; Huang, Thomas S.
Author_Institution :
Univ. of Illinois at Urbana-Champaign, Urbana, IL
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, the problem of person-independent facial expression recognition from 3D facial shapes is investigated. We propose a novel automatic feature selection method based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidean distances between 83 facial feature points in the 3D space. Using a regularized multi-class AdaBoost classification algorithm, we achieve a 95.1% average recognition rate for six universal facial expressions on the publicly available 3D facial expression database BU-3DFE [1], with a highest average recognition rate of 99.2% for the recognition of surprise. We compare these results with the results based on a set of manually devised features and demonstrate that the auto features yield better results than the manual features. Our results outperform the results presented in the previous work [2] and [3], namely average recognition rates of 83.6% and 91.3% on the same database, respectively.
Keywords :
emotion recognition; face recognition; feature extraction; image classification; visual databases; 3D facial expression database; 3D facial expression recognition; AdaBoost classification algorithm; BU-3DFE; automatic feature selection method; normalized Euclidean distances; Classification algorithms; Entropy; Face recognition; Facial features; Humans; Information analysis; Mouth; Shape; Spatial databases; Surface topography;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
Conference_Location :
Anchorage, AK
ISSN :
2160-7508
Print_ISBN :
978-1-4244-2339-2
Electronic_ISBN :
2160-7508
Type :
conf
DOI :
10.1109/CVPRW.2008.4563052
Filename :
4563052
Link To Document :
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