Facial expression analysis under partial occlusion

Six basic facial expressions are investigated when the human face is partially occluded, i.e. when the eyes and eyebrows or the mouth regions are occluded. Such occlusions occur when a person wears glasses (e.g. in VR application) or a mouth mask (e.g. in medical application). More specifically, we are interested in finding the part of the face that contains sufficient information in order to correctly classify these six expressions. Two facial image databases are employed in our experiments. Each image from the database is convolved with a set of Gabor filters having various orientations and frequencies. The new feature vectors are classified by using a maximum correlation classifier and the cosine similarity measure approaches. We find that, overall, the facial expression recognition method provides robustness against partial occlusion, the classification accuracy only decreasing from 89.7% (no occlusion) to 84% (eyes region occlusion) and 83.5% (mouth region occlusion) for the first database and from 94.5% (no occlusion) to 91.5% (eyes region occlusion) and 87.2% (mouth region occlusion) for the second database, respectively.