Paper data
Title:
Facial expression recognition by combination of classifiers Author(s): Dubuisson Séverine, Laboratory Heudiasyc, University of Technology of Compiègne. BP 20529. F60205 Compiègne, France Davoine Franck, Laboratory Heudiasyc, University of Technology of Compiègne. BP 20529. F60205 Compiègne, France Page numbers in the proceedings: Volume I pp 107110 Session: Pattern Recognition and Classification
Paper abstract
In this paper, we present a classifier fusion solution for automatic facial expression recognition. We represent our data using a sorted Principal Component Analysis, followed by a Linear Discriminant Analysis: the selection of principal components first performs a dimensionally reduction by improving discriminant capacities and then, a Linear Discriminant Analysis provides a class representation subspace where new samples can be classified. Using a fuzzy integral method, the classification is operated by combining, the outputs of three classifiers (using Mahalanobis distance, Euclidean distance and a Bayes rule based criterion). This method gives, for a new sample, a probabilistic interpretation of the different classifier outputs to generate a fuzzy measure vector for each considered facial expression class. The sample is then classified into class with maximum fuzzy posterior probability.
Paper
