Katharina Prasse

Papers

Total Citations

H-Index

About

Katharina Prasse is a researcher advancing the field of human-computer interaction and action recognition, with a particular focus on hand gesture analysis. Her work explores the intersection of computer vision and geometric deep learning, where she has introduced innovative methods for interpreting complex hand movements. In her most-cited paper, "Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition" (2024), Prasse demonstrates a novel approach that leverages spherical harmonics to enhance the accuracy of skeleton-based action recognition systems. This technique captures local geometric features of hand poses, enabling more robust and nuanced interpretation of dynamic gestures—a critical step for applications in virtual reality, sign language translation, and robotic control. Though early in her career, her contributions have already garnered attention, with this paper earning 3 citations shortly after publication. Prasse’s work stands out for its elegant blend of mathematical theory and practical implementation, offering a pathway to more natural and intuitive human-machine interfaces. As she continues to refine these methods, her research promises to shape the future of how machines perceive and respond to human motion.