An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition

• URL: http://arxiv.org/abs/2203.12428v1
• Date: Wed, 23 Mar 2022 14:07:39 GMT
• Title: An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition
• Authors: Duy Le Hoai, Eunchae Lim, Eunbin Choi, Sieun Kim, Sudarshan Pant, Guee-Sang Lee, Soo-Huyng Kim, Hyung-Jeong Yang
• Abstract summary: This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. We achieved a macro F1 score of 0.48 on the ABAW challenge validation set compared to 0.39 from the baseline model.
• Score: 6.229820412732652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial Action Coding System is an approach for modeling the complexity of human emotional expression. Automatic action unit (AU) detection is a crucial research area in human-computer interaction. This paper describes our submission to the third Affective Behavior Analysis in-the-wild (ABAW) competition 2022. We proposed a method for detecting facial action units in the video. At the first stage, a lightweight CNN-based feature extractor is employed to extract the feature map from each video frame. Then, an attention module is applied to refine the attention map. The attention encoded vector is derived using a weighted sum of the feature map and the attention scores later. Finally, the sigmoid function is used at the output layer to make the prediction suitable for multi-label AUs detection. We achieved a macro F1 score of 0.48 on the ABAW challenge validation set compared to 0.39 from the baseline model.

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