Henglin Shi
Papers
1
Total Citations
42
H-Index
1
About
Henglin Shi is a leading researcher in affective computing and human behavior analysis, with a focus on leveraging body gestures for emotion recognition. His work addresses a critical gap in artificial intelligence—moving beyond traditional facial expression and speech analysis to explore the rich, non-verbal communicative power of spontaneous gestures. Shi’s most-cited paper, "Analyze Spontaneous Gestures for Emotional Stress State Recognition: A Micro-gesture Dataset and Analysis with Deep Learning" (2019, 42 citations), introduced a novel micro-gesture dataset and deep learning framework that demonstrates how subtle body movements can reliably indicate emotional stress states. This contribution has significant implications for developing more empathetic and context-aware AI systems in robotics, mental health monitoring, and human-computer interaction. By pioneering the use of micro-gestures as a primary emotional cue, Shi has opened new avenues for research in non-verbal behavior analysis. His work is widely recognized for its methodological rigor and practical impact, inspiring further studies on multimodal emotion recognition. For students and researchers, Shi’s research exemplifies how innovative data collection and deep learning can expand the boundaries of affective computing.
Research Focus
Key Achievements
Top Papers
- 1