Abstract
This paper details the method and experiments conducted towards our submission to the FERA 2011 facial expression recognition benchmarking evaluations. The benchmarking evaluation task involves recognizing 5 emotion classes in videos. Our method for detecting facial expressions is a fusion of the decisions of two FER approaches based on two different feature representations, namely using motion information from facial regions and facial feature point displacement information. The main observation motivating the approach we took is that different feature representations are discriminative in detecting different facial expressions. Hence a fusion approach could complement each other to improve recognition performance. Experiments were conducted on the GEMEP-FERA data set provided by the organizers.
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