Facial video based stress detection for enhancing ecological validity.

In contemporary society, high-level stress poses significant detrimental effects on mental and physical well-being, impacting performance in various aspects of life including work, studies, and social interactions. Previous research efforts have primarily relied on the induction of stress through diverse mental tasks under artificial experimental conditions, which may lack ecological validity. This study aimed to address this limitation by collecting facial data without additional contextual interventions during self-introductions from participants. A regression model was developed to evaluate an individual's stress level based solely on their facial expressions captured via video. Utilizing a dataset of 240 participants, the model incorporated both facial videos and perceived stress levels for analysis. Our findings revealed that specific facial areas and features were strongly correlated with perceptions of stress, offering insights into how facial cues can mirror subjective experiences of stress. The regression model achieved impressive performance metrics, attaining a Pearson correlation efficient of 0.539 and internal consistency reliability of 0.70. These results suggest that the model possesses high applicability for early detection and management of stress, particularly by demonstrating an elevated level of ecological validity compared to previous methodologies. The positive outcomes of this study highlight considerable potential for utilizing facial analysis as a tool in identifying stress at an early stage, enabling proactive interventions for stress alleviation. Future research is encouraged to refine the concept further and enhance its accuracy, thereby maximizing its utility in real-world scenarios.