  Diversity of the features extracted by deep neural networks is important for
enhancing the model generalization ability and accordingly its performance in
different learning tasks. Facial expression recognition in the wild has
attracted interest in recent years due to the challenges existing in this area
for extracting discriminative and informative features from occluded images in
real-world scenarios. In this paper, we propose a mechanism to diversify the
features extracted by CNN layers of state-of-the-art facial expression
recognition architectures 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 the state-of-the-art performance of 89.99% on RAF-DB,
89.34% on FER+ and the competitive accuracy of 60.02% on AffectNet dataset.
