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A person's face discloses important information about their affective state.Although there has been extensive research on recognition of facialexpressions, the performance of existing approaches is challenged by facialocclusions. Facial occlusions are often treated as noise and discarded inrecognition of affective states. However, hand over face occlusions can provideadditional information for recognition of some affective states such ascuriosity, frustration and boredom. One of the reasons that this problem hasnot gained attention is the lack of naturalistic occluded faces that containhand over face occlusions as well as other types of occlusions. Traditionalapproaches for obtaining affective data are time demanding and expensive, whichlimits researchers in affective computing to work on small datasets. Thislimitation affects the generalizability of models and deprives researchers fromtaking advantage of recent advances in deep learning that have shown greatsuccess in many fields but require large volumes of data. In this paper, wefirst introduce a novel framework for synthesizing naturalistic facialocclusions from an initial dataset of non-occluded faces and separate images ofhands, reducing the costly process of data collection and annotation. We thenpropose a model for facial occlusion type recognition to differentiate betweenhand over face occlusions and other types of occlusions such as scarves, hair,glasses and objects. Finally, we present a model to localize hand over faceocclusions and identify the occluded regions of the face.

Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions