Rapid and Brief Communication: Recognizing facial action units using independent component analysis and support vector machine

Authors:
Chao-Fa Chuang;Frank Y. Shih
Affiliations:
College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA;College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
Venue:
Pattern Recognition
Year:
2006

Citing 5
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Abstract

Facial expression provides a crucial behavioral measure for studies of human emotion, cognitive processes, and social interaction. In this paper, we focus on recognizing facial action units (AUs), which represent the subtle change of facial expressions. We adopt ICA (independent component analysis) as the feature extraction and representation method and SVM (support vector machine) as the pattern classifier. By comparing with three existing systems, such as Tian, Donato, and Bazzo, our proposed system can achieve the highest recognition rates. Furthermore, the proposed system is fast since it takes only 1.8ms for classifying a test image.