In this paper, we present an automatic algorithm for facial expression recognition. We first propose a method for automatic facial feature extraction, based on the analysis of outputs of local Gabor filters. Such analysis is done using a spatial adaptive triangulation of the magnitude of the filtered images. Then, we propose a classification procedure for facial expression recognition, considering the internal part of registered still faces. Principal Component Analysis allows to represent faces in a low-dimensional space, defined by basis functions that are adapted to training sets of facial expressions. We show how to select the best basis functions for facial expression recognition, providing a good linear discrimination: results prove the robustness of the recognition method. © Springer-Verlag 2001.
CITATION STYLE
Dubuisson, S., Davoine, F., & Cocquerez, J. P. (2001). Automatic facial feature extraction and facial expression recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2091 LNCS, pp. 121–126). Springer Verlag. https://doi.org/10.1007/3-540-45344-x_19
Mendeley helps you to discover research relevant for your work.