Abstract：Each individual makes facial expressions in a unique way. In this paper, a locality-feature-aggregation(LFA) loss function is proposed. Differences between images of the same class are reduced and those between images of different classes are expanded during the training of deep neural network. Thus, the influence of expression polymorphism on feature extraction by deep learning is weakened. The local areas with rich expressions can express facial expression features better. A deep learning network framework incorporating LFA loss function is proposed. Local features of facial images are extracted for facial expression recognition. Compared with other methods, the proposed method is more effective on real world RAF datasets and CK+ datasets under laboratory conditions.
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2020, Vol. 33 
