Yichao Cao is a dedicated forensic science researcher currently affiliated with the Shanghai Research Institute of Criminal Science and Technology and the Shanghai Key Laboratory of Crime Scene Evidence in Shanghai, PR China.His scholarship spans forensic medicine and biomedical engineering, with a special focus on advancing methodologies for face recognition within forensic applications.

Cao’s notable work includes the study titled “A Study of Multi‑Pose Effects on a Face Recognition System,” published in IGMin Research. This investigation addresses how pose variations—such as profiles versus frontal images—impact the accuracy of face recognition, particularly in environments with large-scale image galleries. Through rigorous simulation, his research provides predictive insights into top‑N match rates across varying gallery sizes, offering valuable guidance for deploying practical forensic systems.

By integrating biometric algorithms with real-world forensic challenges, Cao contributes to improving criminal investigations and evidence analysis. His work not only evaluates the robustness of face recognition technology but also examines how pose-related variables can either hinder or enhance the identification process in forensic settings.

Through his position at one of China’s foremost forensic science institutions, Cao merges academic rigor with practical utility. His research aligns with the Key Laboratory’s mission of enhancing crime-scene evidence analysis via cutting-edge science and technology. As forensic imaging and facial recognition increasingly intersect, his expertise makes him a key figure in developing robust, scalable forensic systems for law enforcement and judicial processes.

Yichao Cao’s research primarily centers on forensic science, biometric recognition systems, and crime scene evidence analysis. Working at the Shanghai Research Institute of Criminal Science and Technology and the Shanghai Key Laboratory of Crime Scene Evidence, he focuses on enhancing the accuracy and reliability of face recognition systems in forensic environments. His interests include multi-pose facial recognition, pattern recognition, image processing, and biometric identification technologies. He is particularly drawn to evaluating how pose variations affect facial recognition performance across large databases—critical in real-world criminal investigations. In addition, he explores scalable algorithmic frameworks that optimize identification rates in law enforcement scenarios. Cao is also engaged in forensic image simulation, evidence digitization, and machine learning applications in forensic contexts. His research aims to bridge the gap between academic advancements in biometric AI and practical needs of forensic practitioners, ultimately contributing to more robust, scientifically-grounded criminal justice systems.