Normalized margin loss for action unit detection

Andrei Racoviteanu, Corneliu Florea, Laura Florea, Constantin Vertan

Abstract: This paper addresses the problem of recognition of naturally-appearing human facial movements (action units), as an intermediate step toward their aggregation for the recognition and understanding of facial expressions. With respect to the proposed method, we introduce a domain adaptation solution that is applied to deep convolutional networks, taking advantage of the networks capability of providing simultaneous predictions and discriminative embeddings. In this way, we adapt information gathered from training on mutual expression recognition to facial action unit detection. The described strategy is evaluated in the context of action units in the wild within the EmotioNet dataset and action units acquired in laboratory conditions within the DISFA and CK+ datasets. Our method achieves results comparable to state-of-the-art and demonstrates superior recognition in the case of rarely occurring action units. Additionally, the embedding space structuring is significantly enhanced with respect to the results obtained by classical losses.