Until recently, most robotic systems have operated with limited emotional intelligence, primarily responding to pre-programmed cues rather than adapting to human emotional states. Thus, affect recognition in humanrobot-interaction remains a significant challenge and is twofold: robots must not only detect emotional expressions but they also need to interpret them within their social context, requiring systems that are capable of collecting information from its surrounding, analyzing it and thereafter generalizing across different interaction scenarios and cultural contexts to handle more complex situations.

This thesis tackles affect recognition using multi-modal approaches that combine thermal imaging, facial expression analysis, and contextual understanding. Thermal imaging offers unique insights into physiological responses associated with emotional states, complementing traditional vision-based approaches while maintaining non-contact operation. The integration of thermal imaging, facial expression analysis, and contextual understanding creates a comprehensive multi-modal framework that addresses the key challenges in affect recognition, such as varying lighting conditions, occlusions, and ambiguous emotional expressions. This combination provides complementary information streams that enhance robustness in real-world environments, making it an effective case study for developing context-aware emotional intelligence in robotics.

We introduce a novel context-aware transformer architecture that processes multiple data streams while maintaining temporal relationships and contextual understanding. Each modality contributes complementary information about the user’s emotional state, while the context processing ensures situation-appropriate interpretation. For instance, distinguishing between a smile indicating enjoyment during collaborative tasks versus one masking nervousness in stressful situations. This contextual awareness is crucial for appropriate robot responses in real-world deployments.

The research contributions span four areas: (1) developing robust thermal feature extraction techniques that capture subtle emotional responses (2) creating a transformer-based architecture for multi-modal fusion that effectively incorporates situational information, (3) implementing real-time processing pipelines that enable practical deployment in human-robot interaction scenarios, and (4) validating these approaches through extensive real-world interaction studies. Results show improved recognition accuracy from 77% using traditional approaches to 89% with our context-aware multi-modal system, demonstrating the ability to understand and appropriately respond to human emotions in dynamic social situations.