Local Subclass Constraint for Facial Expression Recognition in the Wild

The Automated Facial Expression Recognition (FER) in the wild is still a challenge problem. Currently, most of Deep Convolutional Neural Networks(DCNNs) based FER methods adopt softmax cross-entropy loss to encourage the separability of inter-class features. Many deep embedding approaches (e.g. contrastive loss, triplet loss, center loss) have been extended to the field of FER to enhance the discriminative ability of deep expression features and obtain the predictive effect. In this work, we present a novel deep embedding approach explicitly designed to respect the huge intra-class variation of expression features while learning discriminative expression features. We aim at forming a locally compact representation space structure through minimizing the distance between samples and their nearest subclass center. We demonstrate the effectiveness of this idea on RAF(Real-world Affective Faces) database. The experiment results show that our approaches can not only improve the classification performance but also adaptively learn a locally compact and expression intensity-aware feature space structure. We further extend our models to Static Facial Expressions in the Wild (SFEW) dataset and the results show the generalized ability of our approaches.