CARD: Contactless Affect Recognition Database Using Young Adults of Premier Indian University

Abstract

Emotional distress and declining psychological well-being are increasingly prevalent among student populations in India, emerging as a significant concern relative to other societal segments. In the context of computational social systems, especially within human–device interaction (HDI) and social media–mediated communication, addressing these mental health (MH) challenges demands a nuanced understanding of affective states through both verbal expression and subtle nonverbal behavior. This creates technical and methodological obstacles for nonintrusive HDI systems, including the need to simulate naturalistic interaction settings, capture various social signals, decode authentic emotional responses, and ensure high fidelity in emotion recognition without relying on obtrusive sensing technologies. This study aimed to assess the emotional well-being of young adults in premier Indian institutions through various technological interventions during machine interactions. However, the lack of standardized noncontact databases has hindered the development and evaluation of emotional health assessment tools. To address this gap, we developed the Contactless Affect Recognition Database (CARD), built from data gathered across 53 participants, which is designed to support the advancement of robust and scalable models for emotion and mental disorder detection, particularly in online and social media–enabled environments such as video conferencing, live streaming, and virtual classrooms. The database includes RGB and infrared facial images, peak facial clips, breath, heart waveforms, and other physiological parameters recorded during machine interactions, enabling cross-platform applications from controlled laboratory settings to real-world social media contexts.