Facial expression recognition using pseudo 3-D Hidden Markov models

In this paper pseudo 3-D Hidden Markov Models (P3DHMMs) are applied to the task of dynamic facial expression recognition. P3DHMMs are an extension of the pseudo 2-D case, which has been successfully used for the classification of images and the recognition of faces. Although the application of P3DHMMs for image sequence recognition has been reported before, this paper provides a formal definition of the novel approach as well as a detailed explanation of a triple embedded Viterbi algorithm. Furthermore an equivalent one-dimensional structure is introduced, which allows the application of the standard Viterbi and Baum-Welch-Algorithms. The approach has been evaluated on a person independent database, which consists of 4 different facial expressions, performed by 6 individuals. The recognition accuracy achieved in the experiments is close to 90%.