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About

Huanjing Yue is a leading researcher in affective computing, computer vision, and non-line-of-sight (NLOS) imaging. Her work focuses on advancing facial expression and action unit (AU) detection—critical for social robotics and safe driving—by developing innovative correlation learning frameworks. Her 2024 paper on multi-scale self-adjusting correlation learning for AU detection (19 citations) introduces a method to capture the rich anatomical relationships between facial AUs, significantly improving emotion recognition accuracy. Yue also pioneers practical inspection technologies, as shown in her 2019 work on automatic gauge detection via geometric fitting (16 citations), which enhances safety monitoring in high-voltage electrical substations using robotic vision. Addressing privacy and miniaturization, she explores lensless sensing for facial expression recognition (2024), transforming spectral attention features to enable compact, privacy-preserving systems. Most recently, her 2025 research on reprojection-guided NLOS imaging tackles irregular undersampling, promising breakthroughs in robotic vision and autonomous navigation by visualizing scenes beyond direct line of sight. With a growing citation record and contributions spanning from real-world safety inspection to cutting-edge NLOS imaging, Yue’s work bridges theoretical innovation and practical deployment, making her a notable figure in vision-based human-computer interaction and sensing.