Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition

Authors

  • Daeha Kim Inha University
  • Byung Cheol Song Inha University

Keywords:

Emotional Intelligence

Abstract

Since most facial emotion recognition (FER) methods significantly rely on supervision information, they have a limit to analyzing emotions independently of persons. On the other hand, adversarial learning is a well-known approach for generalized representation learning because it never requires supervision information. This paper presents a new adversarial learning for FER. In detail, the proposed learning enables the FER network to better understand complex emotional elements inherent in strong emotions by adversarially learning weak emotion samples based on strong emotion samples. As a result, the proposed method can recognize the emotions independently of persons because it understands facial expressions more accurately. In addition, we propose a contrastive loss function for efficient adversarial learning. Finally, the proposed adversarial learning scheme was theoretically verified, and it was experimentally proven to show state of the art (SOTA) performance.

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Published

2021-05-18

How to Cite

Kim, D., & Song, B. C. (2021). Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 5948-5956. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16743

Issue

Section

AAAI Technical Track on Humans and AI