Abstract
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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Dubuisson, S., Davoine, F., Cocquerez, JP. (2001). Automatic Facial Feature Extraction and Facial Expression Recognition. In: Bigun, J., Smeraldi, F. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2001. Lecture Notes in Computer Science, vol 2091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45344-X_19
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DOI: https://doi.org/10.1007/3-540-45344-X_19
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