@article{HU2025, 
author = {Can HU and Jun ZHU and Hongcheng MEI and Hongling GUO and Yajun LI and Xianhe DENG and Zhi LI},
title = {Application of artificial intelligence technology to trace evidence identification},
year = {2025},
journal = {Journal of Capital Normal University (Natural Science Edition)},
volume = {46},
number = {5},
pages = {1-7},
keywords = {artificial intelligence, forensic science, trace evidence, examination and identification},
url = {https://www.sciopen.com/article/10.19789/j.1004-9398.2025.05.001},
doi = {10.19789/j.1004-9398.2025.05.001},
abstract = {Trace evidence identification is a vital aspect of forensic identification but is facing methodological challenges since the utilized analytical methods have slow processing speeds, limited accuracy, and a reliance on expert experience. Recently, artificial intelligence (AI) technologies have introduced innovative solutions for the recognition, analysis, and comparison of trace evidence, greatly enhancing both efficiency and accuracy, particularly machine learning, computer vision, and deep learning. This paper reviews the application of AI technology in the examination of trace evidence, including aspects such as spectral analysis, microscopic image analysis, and result interpretation. The roles of AI were discussed in respect of improving the efficiency and accuracy of forensic examinations, with some of its future development prospects.}
}