Zihan Geng
Papers
1
Total Citations
30
H-Index
1
About
Zihan Geng is a researcher at the forefront of privacy-preserving computer vision, with a primary focus on human pose estimation and secure imaging systems. Their most notable contribution is the development of an image-free single-pixel keypoint detection method, which directly extracts human skeletal keypoints from optically encoded measurements without reconstructing identifiable images. This groundbreaking approach, published in 2024 and already garnering 30 citations, addresses critical privacy concerns in surveillance, identification, and robot vision by eliminating the need to capture or store human images. Geng’s work represents a paradigm shift in balancing utility and privacy, enabling practical applications like human-robot interaction and activity monitoring while safeguarding personal data. By pioneering this compressed sensing-based technique, they have opened new avenues for privacy-compliant computer vision systems. Their research continues to influence the development of secure, efficient algorithms that protect individual privacy without sacrificing performance, making them a rising voice in ethical AI and visual sensing technologies.
Research Focus
Key Achievements
Top Papers
- 1