DocumentCode :
3134203
Title :
3D facial expression recognition based on properties of line segments connecting facial feature points
Author :
Tang, Hao ; Huang, Thomas S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL
fYear :
2008
fDate :
17-19 Sept. 2008
Firstpage :
1
Lastpage :
6
Abstract :
The 3D facial geometry contains ample information about human facial expressions. Such information is invariant to pose and lighting conditions, which have imposed serious hurdles on many 2D facial analysis problems. In this paper, we perform person and gender independent facial expression recognition based on properties of the line segments connecting certain 3D facial feature points. The normalized distances and slopes of these line segments comprise a set of 96 distinguishing features for recognizing six universal facial expressions, namely anger, disgust, fear, happiness, sadness, and surprise. Using a multi-class support vector machine (SVM) classifier, an 87.1% average recognition rate is achieved on the publicly available 3D facial expression database BU-3DFE. The highest average recognition rate obtained in our experiments is 99.2% for the recognition of surprise. Our result outperforms the result reported in the prior work, which uses elaborately extracted primitive facial surface features and an LDA classifier and which yields an average recognition rate of 83.6% on the same database.
Keywords :
computational geometry; emotion recognition; face recognition; image classification; support vector machines; 3D facial expression recognition; 3D facial geometry; SVM classifier; facial feature point; line segment property; multiclass support vector machine; Face recognition; Facial features; Humans; Information analysis; Information geometry; Joining processes; Linear discriminant analysis; Spatial databases; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on
Conference_Location :
Amsterdam
Print_ISBN :
978-1-4244-2153-4
Electronic_ISBN :
978-1-4244-2154-1
Type :
conf
DOI :
10.1109/AFGR.2008.4813304
Filename :
4813304
Link To Document :
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