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This special session expands the lens from facial expression-based analysis to the broader paradigm of multimodal affective computing, exploring how the integration of visual, auditory, physiological, and contextual data is transforming our understanding of human emotion. While macro- and micro-expression analysis remains a key focus, the session broadens its scope to include multimodal approaches—such as vocal tone, gesture, language, biometric signals, and environmental context—that together enable more accurate and context-aware emotion recognition. The session examines the scientific foundations, real-world applications, and the pressing ethical and security challenges posed by these increasingly complex systems. As multimodal technologies gain traction across tourism, healthcare, education, humancomputer interaction, and security domains, their development demands humancentered design, regulatory foresight, and rigorous ethical safeguards. The session aims to reinforce a shared commitment to balancing technological innovation with societal responsibility, ensuring that affective computing systems enhance well-being and trust without compromising fundamental rights.

Topics of Interest

The special session is seeking forward-looking perspectives on:

• Multimodal fusion architectures for robust emotion inference
• Cross-cultural validation of emotion datasets
• Explainable AI (XAI) for affective computing systems
• Ethical-by-design frameworks embedded in system architecture
• Real-world deployment and human-centered evaluation

This special session will also explore application use cases, particularly in following domains:

1. Tourism and Hospitality: emotion recognition technologies are increasingly being integrated into smart tourism ecosystems. Applications include:
• Real-time visitor sentiment analysis at airports, museums, and attractions
• Adaptive service personalization in hotels and theme parks
• Experience optimization through behavioral analytics
2. Healthcare and Mental Health: in clinical settings, emotion recognition offers promising support tools for:
• Early detection of depression, anxiety, and stress-related disorders
• Monitoring emotional states in telemedicine environments
• Assisting patients with communication impairments (e.g., autism spectrum conditions)
• Psychological Autonomy: The moral implications of inferring internal states without explicit disclosure

Finally, the session will emphasize security and risk assessment, focusing on ethics, privacy, and security—particularly regarding the ethical governance of emotion AI and its key concerns:

• Privacy and Surveillance: The covert capture and analysis of facial data in public spaces
• Algorithmic Bias: Disparities in accuracy across demographic groups
• Data Security: Risks of biometric data breaches and misuse
• Informed Consent: Ensuring individuals understand when and how their emotional data is analyzed