저연산 컴퓨팅 모듈에서 구동 가능한 안면 복합 특성 분석 기반 대상자 긴장 상태 판단에 관한 연구

Abstract

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.