Discrete Choice Models for Static Facial Expression Recognition

In this paper we propose the use of Discrete Choice Analysis (DCA) for static facial expression classification. Facial expressions are described with expression descriptive units (EDU), consisting in a set of high level features derived from an active appearance model (AAM). The discrete choice model (DCM) is built considering the 6 universal facial expressions plus the neutral one as the set of the available alternatives. Each alternative is described by an utility function, defined as the sum of a linear combination of EDUs and a random term capturing the uncertainty. The utilities provide a measure of likelihood for a combinations of EDUs to represent a certain facial expression. They represent a natural way for the modeler to formalize her prior knowledge on the process. The model parameters are learned through maximum likelihood estimation and classification is performed assigning each test sample to the alternative showing the maximum utility. We compare the performance of the...

• Type: Conference
• Year: 2006
• Where: ACIVS
• Authors: Gianluca Antonini, Matteo Sorci, Michel Bierlaire, Jean-Philippe Thiran
• Keywords: ACIVS 2006, Computer Vision, Discrete Choice, Facial Expression, Static Facial Expression