Dingkang Yang
Papers
1
Total Citations
13
H-Index
1
About
Dingkang Yang is a leading researcher in affective computing and human-robot interaction, with a particular focus on advancing automatic facial expression recognition (FER) for socially intelligent robots. His work addresses the critical challenge of enabling machines to accurately interpret human emotions in real-world, interactive settings—a cornerstone for applications in companion robotics and intelligent healthcare. Yang’s most cited paper, "MGR³Net: Multigranularity Region Relation Representation Network for Facial Expression Recognition in Affective Robots" (2024, 13 citations), introduces a novel deep learning framework that captures both local and global facial features by modeling region relations at multiple granularities. This approach significantly improves FER robustness against occlusions, pose variations, and subtle expression changes, overcoming key limitations in existing models. By bridging the gap between high-accuracy laboratory benchmarks and the noisy, dynamic conditions of human-robot interaction, Yang’s contributions are paving the way for more empathetic and responsive autonomous systems. His work is widely recognized for its practical impact on next-generation affective robots designed for companionship and healthcare support.
Research Focus
Key Achievements
Top Papers
- 1