LEARNING DIVERSIFIED FEATURE REPRESENTATIONS FOR FACIAL EXPRESSION RECOGNITION IN THE WILD

Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability in different learning tasks. Facial expression recognition in the wild has attracted interest recently due to the challenges existing for extracting discriminative features from occluded images in real-world scenarios. In this paper, we propose a mechanism to diversify the features extracted by CNN layers of facial expression recognition models for enhancing the model capacity in learning discriminative features. To evaluate the effectiveness of the proposed approach, we incorporate this mechanism in two state-of-the-art models to (i) diversify local/global features in an attention-based model and (ii) diversify features extracted by different learners in an ensemble-based model. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+ and RAF-DB, show the effectiveness of our method, achieving state-of-the-art performance of 89.99% on RAF-DB, 89.34% on FER+ and the competitive accuracy of 60.02% on AffectNet.