Xiangrui Li

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Xiangrui Li is a researcher at the forefront of biometric security and IoT-driven identity systems. Their work centers on the fusion of advanced imaging techniques and machine learning to enhance authentication in interconnected environments. Li’s most-cited paper, “Gender Classification Based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images in the IoT Environment” (2024, 12 citations), introduces a novel method that combines spatial and frequency-domain features from optical coherence tomography (OCT) fingerprint images. This approach not only improves gender classification accuracy but also addresses critical privacy and security challenges in the Internet of Things (IoT), where device-to-device communication demands robust, non-invasive biometric solutions. By leveraging OCT’s subsurface imaging capabilities, Li’s research pushes beyond traditional fingerprint analysis, offering deeper biometric insights while mitigating spoofing risks. Although early in their career, Li’s contributions are already shaping the next generation of secure, context-aware authentication systems. Their work underscores a commitment to bridging the gap between biometric science and practical IoT security, making them a rising voice in the field of intelligent, privacy-preserving identification.