Abstract
This work deals with how the machine classifies human facial expressions, which has been a challenging problem for many researchers from the diverse areas. The facial expression recognition system mainly consists of two cascade stages: the representation method for the facial images at the front and the facial emotion classifier at the back. The Gabor-wavelets based method has shown promising performance because of its efficient representation and biological implication. Here we focus on the classification method to obtain high recognition rate of facial expressions. Results suggest that enhanced Fisher discrimination model, which had been used for the face recognition task, outperformed Principal Component Analysis (PCA) based classifier (or the neural network) with the 93% correction rate, when it is combined with the Gabor representation.
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Lee, SO., Kim, YG., Park, GT. (2003). Facial Expression Recognition Based upon Gabor-Wavelets Based Enhanced Fisher Model. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_61
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DOI: https://doi.org/10.1007/978-3-540-39737-3_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20409-1
Online ISBN: 978-3-540-39737-3
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