TY - JOUR AU - Kim, Daeha AU - Song, Byung Cheol PY - 2021/05/18 Y2 - 2024/03/29 TI - Contrastive Adversarial Learning for Person Independent Facial Emotion Recognition JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 7 SE - AAAI Technical Track on Humans and AI DO - 10.1609/aaai.v35i7.16743 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16743 SP - 5948-5956 AB - 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. ER -