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Analysis, interpretation, and recognition of facial action units and expressions using neuro-fuzzy modeling

Khademi, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-12159-3_15
  3. Publisher: 2010
  4. Abstract:
  5. In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation of the facial expressions, and 3) by continuous values of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vector machines, hidden Markov models, and neural network classifiers
  6. Keywords:
  7. Biased discriminant analysis (BDA) ; Facial action units (AUs) ; Neuro-fuzzy modeling ; Classifier design and evaluation ; Facial action ; Hybrid learning ; Classifiers ; Discriminant analysis ; Fuzzy inference ; Hidden Markov models ; Hierarchical systems ; Pattern recognition ; Neural networks
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11 April 2010 through 13 April 2010 ; Volume 5998 LNAI , April , 2010 , Pages 161-172 ; 03029743 (ISSN) ; 9783642121586 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-12159-3_15