Self-supervised Contrastive Learning of Multi-view Facial Expressions

• URL: http://arxiv.org/abs/2108.06723v1
• Date: Sun, 15 Aug 2021 11:23:34 GMT
• Title: Self-supervised Contrastive Learning of Multi-view Facial Expressions
• Authors: Shuvendu Roy, Ali Etemad
• Abstract summary: Facial expression recognition (FER) has emerged as an important component of human-computer interaction systems. We propose Contrastive Learning of Multi-view facial Expressions (CL-MEx) to exploit facial images captured simultaneously from different angles towards FER.
• Score: 9.949781365631557
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Facial expression recognition (FER) has emerged as an important component of human-computer interaction systems. Despite recent advancements in FER, performance often drops significantly for non-frontal facial images. We propose Contrastive Learning of Multi-view facial Expressions (CL-MEx) to exploit facial images captured simultaneously from different angles towards FER. CL-MEx is a two-step training framework. In the first step, an encoder network is pre-trained with the proposed self-supervised contrastive loss, where it learns to generate view-invariant embeddings for different views of a subject. The model is then fine-tuned with labeled data in a supervised setting. We demonstrate the performance of the proposed method on two multi-view FER datasets, KDEF and DDCF, where state-of-the-art performances are achieved. Further experiments show the robustness of our method in dealing with challenging angles and reduced amounts of labeled data.

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