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Facial Action Units Detection (FAUs) problem focuses on identifying various detail units expressing on the human face, as defined by the Facial Action Coding System, which constitutes a fine-grained classification problem. This is a challenging task in computer vision. In this study, we propose a Prompt Tuning approach to address this problem, involving a 2-step training process. Our method demonstrates its effectiveness on the Affective in the Wild dataset, surpassing other existing methods in terms of both accuracy and efficiency.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A3A04036408) and also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub).