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Latent Semantic Analysis of Facial Action Codes for Automatic Facial Expression Recognition

B. Fasel, F. Monay, and D. Gatica-Perez,
International Conference on Multimedia, Workshop on Video Surveillance and Sensor Networks (ACM MM-VSSN)
New York, USA, October 2004

Abstract

For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. One possibility is to label facial expressions into emotion categories. Another approach is to label facial expressions independently from any interpretation attempts. This can be achieved via the facial action coding system (FACS). In this paper we present a novel approach that allows to automatically cluster FACS codes into meaningful categories. Our approach exploits the fact that FACS codes can be seen as documents containing terms -the action units (AUs) present in the codes- and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. The FACS code derived descriptions are computed by Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA). We show that, as a high-level description of facial actions, the newly derived codes constitute a competitive alternative to both basic emotion and FACS codes. We have used them to train different types of artificial neural networks.


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@InProceedings{eth_biwi_00334,
  author = {B. Fasel and F. Monay and and D. Gatica-Perez and},
  title = {Latent Semantic Analysis of Facial Action Codes for Automatic Facial Expression Recognition},
  booktitle = {International Conference on Multimedia, Workshop on Video Surveillance and Sensor Networks (ACM MM-VSSN)},
  year = {2004},
  month = {October},
  publisher = {ACM},
  keywords = {Latent Semantic Analysis, Automatic Facial Expression Recognition}
}