Comparing a Transferable Belief Model Capable of Recognizing Facial Expressions with the Latest Human Data

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Despite significant amount of research on automatic classification of facial expressions, recognizing a facial expression remains a complex task to be achieved by a computer vision system. Our approach is based on a close look at the mechanisms of the human visual system, the best automatic facial expression recognition system yet. The proposed model is made for the classification of the six basic facial expressions plus Neutral on static frames based on the permanent facial features deformations using the Transferable Belief Model. The aim of the proposed work is to understand how the model behaves in the same experimental conditions as the human observer, to compare their results and to identify the missing informations so as to enhance the model performances. To do this we have given our TBM based model the ability to deal with partially occluded stimuli and have compared the behavior of this model with that of humans in a recent experiment, in which human participants had to classify the studied expressions that were randomly sampled using Gaussian apertures. Simulations show first the suitability of the TBM to deal with partially occluded facial parts and its ability to optimize the available information to take the best possible decision. Second they show the similarities of the human and model observers performances. Finally, we reveal important differences between the use of facial information in the human and model observers, which open promising perspectives for future developments of automatic systems.