A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier

Facial Expression Recognition (FER) is inherently data driven. Spontaneous expressions are substantially different from posed expressions. Spontaneous facial expressions are more challenging and more difficult to recognize. Any facial expression can be represented in many different patterns of muscles movements. Moreover, the facial expressions show discrepancies in different cultures and ethnicities. Therefore, a FER system has to learn a huge problem/feature space. A base classifier trained on a sub-region of feature space, cannot perform equally well in other areas of feature space. Therefore, a base classifier should be assigned higher voting weight in the regions near to its training space and a lower voting weight in the regions far from its training space. In order to maintain high accuracy and robustness of a FER system in space and time, a Dynamic Weight Majority Voting (DWMV) mechanism for base classifiers is introduced. An ensemble system for FER is proposed that has the aptitude of incrementally learning and thus, can learn all possible patterns of expressions that may be generated in feature or in various cultures and ethnicities. Speeded-Up Robust Features (SURF) are used to represent the feature space. Since no work in literature is found on “which similarity measure is more appropriate in SURF descriptor domain for facial expression recognition”, therefore, different similarity measures are used and the results are compared. A vast range of experimentation is performed on posed and spontaneous databases that demonstrates promising results.