Ting-Fu Huang

National Taiwan University of Science and Technology

Papers

Total Citations

H-Index

About

Ting-Fu Huang is a researcher focused on advancing computer vision and robotics, with a particular emphasis on deep learning applications for security and autonomous systems. His work addresses critical challenges in face recognition for indoor security robots, tackling environmental uncertainties such as perceptual aliasing, occlusion, illumination changes, and viewpoint variations. His most-cited paper, "Deep Learning based Face Recognition for Security Robot" (2022), has garnered 4 citations and lays foundational groundwork for improving robotic perception in real-world, dynamic settings. By integrating deep learning techniques, Huang contributes to making security robots more reliable and adaptive, enhancing their ability to identify individuals despite complex environmental factors. His research holds promise for practical deployments in surveillance and safety, bridging the gap between theoretical AI models and robust robotic performance. Huang’s efforts underscore a commitment to solving pressing issues in autonomous navigation and human-robot interaction, positioning him as a developing voice in the intersection of deep learning and robotics.