Anthony T. S. Ho

Papers

Total Citations

H-Index

About

Anthony T. S. Ho is a leading researcher in pattern recognition, computer vision, and biometric security, with a particular focus on facial emotion analysis and graph-based learning. His most cited work, "Emotion recognition from scrambled facial images via many graph embedding" (2017, 62 citations), introduces a novel approach that leverages manifold learning to extract discriminative features from partially occluded or scrambled facial data—a significant challenge in real-world emotion detection. This contribution has advanced the robustness of affective computing systems, enabling more reliable human-computer interaction in uncontrolled environments. Ho’s research bridges theoretical graph embedding techniques with practical applications in security and psychology, demonstrating how low-dimensional representations can preserve critical emotional cues even under data degradation. Beyond this landmark paper, his broader portfolio explores multimedia forensics and image authentication, reflecting a sustained commitment to safeguarding digital integrity. With over 60 citations on this single work alone, Ho’s impact is evident in the growing adoption of manifold-based methods for facial analysis. His work continues to inspire researchers seeking to merge geometric data analysis with human-centric computing, making him a notable figure in the evolving landscape of intelligent vision systems.