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Recognition of facial expressions in presence of partial occlusion

Nowadays, both computer vision researchers and psychology experts show an increased interest for human facial expression analysis. Despite the huge amount of research that has been dedicated to this area, almost all of them concern data recorded in controlled laboratory conditions, which does not always reflect the real world environment in which the human face is partially occluded. Six basic facial expressions are investigated in that case, i.e.when the eyes and eyebrows or the mouth regions are left out. We are interested in finding which part of the face comprised sufficient information with respect to the entire face, in order to correctly classify these six expressions. Each image from the two databases used is convolved with a set of Gabor filters having various orientations and frequencies. The new feature vectors are classified using a maximum correlation classifier and the cosine similarity measure approaches. Overall, the method provides robustness against partial occlusion.