Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition

• URL: http://arxiv.org/abs/2311.06852v1
• Date: Sun, 12 Nov 2023 14:05:09 GMT
• Title: Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition
• Authors: Shuvendu Roy, Ali Etemad
• Abstract summary: We propose ViewFX, a novel view-invariant FER framework based on contrastive learning. We test the proposed framework on two public multi-view facial expression recognition datasets.
• Score: 27.75143621836449
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Although there has been much progress in the area of facial expression recognition (FER), most existing methods suffer when presented with images that have been captured from viewing angles that are non-frontal and substantially different from those used in the training process. In this paper, we propose ViewFX, a novel view-invariant FER framework based on contrastive learning, capable of accurately classifying facial expressions regardless of the input viewing angles during inference. ViewFX learns view-invariant features of expression using a proposed self-supervised contrastive loss which brings together different views of the same subject with a particular expression in the embedding space. We also introduce a supervised contrastive loss to push the learnt view-invariant features of each expression away from other expressions. Since facial expressions are often distinguished with very subtle differences in the learned feature space, we incorporate the Barlow twins loss to reduce the redundancy and correlations of the representations in the learned representations. The proposed method is a substantial extension of our previously proposed CL-MEx, which only had a self-supervised loss. We test the proposed framework on two public multi-view facial expression recognition datasets, KDEF and DDCF. The experiments demonstrate that our approach outperforms previous works in the area and sets a new state-of-the-art for both datasets while showing considerably less sensitivity to challenging angles and the number of output labels used for training. We also perform detailed sensitivity and ablation experiments to evaluate the impact of different components of our model as well as its sensitivity to different parameters.

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