Learning Diversified Feature Representations for Facial Expression Recognition in the Wild

TL;DR

This paper introduces a feature diversification mechanism for CNNs to improve facial expression recognition in challenging real-world images, leading to state-of-the-art results on multiple datasets.

Contribution

It proposes a novel method to diversify features in CNNs, enhancing discriminative power for facial expression recognition in the wild.

Findings

Achieved 89.99% accuracy on RAF-DB

Achieved 89.34% accuracy on FER+

Achieved 60.02% accuracy on AffectNet

Abstract

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.