Authors:
Yu Li Xue
;
Xia Mao
and
Qing Chang
Affiliation:
Beihang University, China
Keyword(s):
Facial Action Unit Recognition, Gabor Wavelet, Optical Flow, Support Vector Machine, Dynamic Bayesian Network.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
Human facial expression is extremely abundant, and can be described by numerous facial action units. Recognizing facial action units helps catching the inner emotion or intention of human. In this paper, we propose a novel method for facial action unit recognition and inference. We used Gabor wavelet and optical flow for feature extraction, and used support vector machine and dynamic bayesian network for classification and inference respectively. We combined the advantages of both global and local feature extraction, recognized the most discriminant AUs with multiple classifiers to achieve high recognition rate, and then inference the related AUs. Experiments were conducted on the Cohn-Kanade AU-Coded database. The results demonstrated that compared to early researches for facial action units recognition, our method is capable of recognizing more action units and achieved good performance.