Recognizing facial action units using independent component analysis and support vector machine

Chao F. Chuang, Frank Shih

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

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 1.8 ms for classifying a test image.

Original languageEnglish (US)
Pages (from-to)1795-1798
Number of pages4
JournalPattern Recognition
Volume39
Issue number9
DOIs
StatePublished - Sep 1 2006

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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