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Recognizing combinations of facial action units with different intensity using a mixture of hidden Markov models and neural network

Khademi, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-12127-2-31
  3. Publisher: 2010
  4. Abstract:
  5. Facial Action Coding System consists of 44 action units (AUs) and more than 7000 combinations. Hidden Markov models (HMMs) classifier has been used successfully to recognize facial action units (AUs) and expressions due to its ability to deal with AU dynamics. However, a separate HMM is necessary for each single AU and each AU combination. Since combinations of AU numbering in thousands, a more efficient method will be needed. In this paper an accurate real-time sequence-based system for representation and recognition of facial AUs is presented. Our system has the following characteristics: 1) employing a mixture of HMMs and neural network, we develop a novel accurate classifier, which can deal with AU dynamics, recognize subtle changes, and it is also robust to intensity variations, 2) although we use an HMM for each single AU only, by employing a neural network we can recognize each single and combination AU, and 3) 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 temporal information involved in formation of the facial expressions. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with other classifiers
  6. Keywords:
  7. Hidden Markov models (HMMs) ; Neural network (NN) ; Action Unit ; Appearance based ; Classifier design and evaluation ; Cohn-Kanade database ; Dimension reduction techniques ; Efficient method ; Facial action ; Facial Action Coding System ; Facial action units (AUs) ; Facial Expressions ; Illumination changes ; Intensity variations ; Temporal information ; Time sequences ; Classifiers ; Data fusion ; Face recognition ; Gesture recognition ; Information fusion ; Neural networks ; Hidden Markov models
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7 April 2010 through 9 April 2010 ; Volume 5997 LNCS , April , 2010 , Pages 304-313 ; 03029743 (ISSN) ; 9783642121265 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007/978-3-642-12127-2_31