JunGyu Lee Lee Sang Hyun Nam, Gi Pyo 2024-12-02T04:00:17Z 2024-12-02T04:00:17Z 2024-11-25 2024-06-27 https://pubs.kist.re.kr/handle/201004/151264 https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11890880 Recent studies have utilized external biosignals, particularly those observable on the human face such as expressions and eye blinks, for the analysis of a subject’s level of anxiety. This paper presents the first comprehensive system for contact-free prediction of various facial features from RGB images, surpassing traditional methods reliant on prior knowledge or significant computing power. Our approach introduces modules for analyzing variations in facial features, such as eye blinks, pupil movement, and expressions, developed to operate effectively in low-end hardware environments with limited computing power. Each module has been validated on its respective benchmark dataset, demonstrating comparability with or even exceeding the State-Of-The-Art. A method for deriving a composite anxiety score based on the combination of biological signal changes is further proposed, with additional validation on a privately-conducted user study. Korean 대한전자공학회 저연산 컴퓨팅 모듈에서 구동 가능한 안면 복합 특성 분석 기반 대상자 긴장 상태 판단에 관한 연구 Study on Facial Composite Feature Analysis for Determining Subject Anxiety Levels on Low-Power Computing Modules Conference 2 2024년도 대한전자공학회 하계학술대회 2024년도 대한전자공학회 하계학술대회 KO 제주 롯데호텔 2024-06-26 2024년도 대한전자공학회 하계학술대회 논문집