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This study improves the recognition accuracy and execution time of
facial expression recognition system. Various techniques were utilized to
achieve this. The face detection component is implemented by the adoption
of Viola-Jones descriptor. The detected face is down-sampled by Bessel
transform to reduce the feature extraction space to improve processing time
then. Gabor feature extraction techniques were employed to extract
thousands of facial features which represent various facial deformation
patterns. An AdaBoost-based hypothesis is formulated to select a few
hundreds of the numerous extracted features to speed up classification. The
selected features were fed into a well designed 3-layer neural network
classifier that is trained by a back-propagation algorithm. The system is
trained and tested with datasets from JAFFE and Yale facial expression
databases. An average recognition rate of 96.83% and 92.22% are registered
in JAFFE and Yale databases, respectively. The execution time for a 100x100
pixel size is 14.5ms. The general results of the proposed techniques are
very encouraging when compared with others.
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