Exploring Vision-Based Features for Detecting Deception in Well-Being: A Cross-Domain Comparison

Deception detection has been extensively studied using vision-based features in domains such as crime, finance, and social interaction. However, little attention has been given to how deception manifests visually in self-reported well-being-a critical area for behavioral health, where inaccurate reporting may affect treatment outcomes and the therapeutic alliance. While clinicians often rely on visual cues such as gaze, facial expressions, and body language to assess deception, these cues remain underutilized in AI-based deception detection in wellbeing scenarios. This study explores vision-based features of deception in the well-being domain and compares them with those from three other domains: biography, academics, and crime. Using mock interview data, we extract facial landmarks, body gestures, and facial action units (AUs) using four feature selection methods. We then visualize and analyze the spatial distribution of features associated with truthful and deceptive responses. Results show that well-being features are generally fewer and more localized-particularly around the nose ridge-with unique presence of eye landmarks and limited hand gestures. In contrast, biography and academics show broader facial and body engagement, while crime displays no differentiation between truth- and deception-related features and lacks emotional AU combinations. AUs associated with joy (AU 6 and AU 12) appear consistently across well-being, academics, and biography, suggesting some domain-agnostic cues. Overall, our findings indicate that most visual features relevant to deception are domain-specific. This highlights the importance of contextaware approaches in deception modeling and supports the development of more reliable, human-centered AI tools for wellbeing assessment and mental health applications.