Akiba Norimitsu, Nakamura Atsushi, Sota Takayuki, Hibino Kazuhito, Kakuda Hidetoshi, Aalders Maurice C G
Physics Section, National Research Institute of Police Science, Kashiwa, Chiba, Japan.
Waseda Research Institute for Science and Engineering, Waseda University, Tokyo, Japan.
J Forensic Sci. 2022 May;67(3):1208-1214. doi: 10.1111/1556-4029.14969. Epub 2022 Jan 5.
Overlapping fingerprints are often found at crime scenes, but only individual fingerprints separated from each other are admissible as evidence in court. Fingerprint components differ slightly among individuals, and thus their fluorescence spectra also differ from each other. Therefore, the separation of overlapping fingerprints using the difference of the fluorescence spectrum was performed with a hyperspectral imager. Hyperspectral data (HSD) of overlapping fingerprints were recorded under UV LED excitation. Principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) were applied to the HSD to determine the optimal method for obtaining high-contrast images of individual fingerprints. The results suggested that MCR-ALS combined with PCA-based initialization is capable of separating overlapping fingerprints into individual fingerprints. In this study, a method for separating overlapping fingerprints without initial parameters was proposed.
在犯罪现场经常能发现重叠的指纹，但只有彼此分离的个体指纹才能在法庭上作为证据被采纳。个体之间的指纹成分略有不同，因此它们的荧光光谱也彼此不同。因此，使用高光谱成像仪通过荧光光谱差异对重叠指纹进行分离。在紫外发光二极管激发下记录重叠指纹的高光谱数据（HSD）。将主成分分析（PCA）和多元曲线分辨交替最小二乘法（MCR-ALS）应用于高光谱数据，以确定获得个体指纹高对比度图像的最佳方法。结果表明，MCR-ALS结合基于PCA的初始化能够将重叠指纹分离为个体指纹。本研究提出了一种无需初始参数即可分离重叠指纹的方法。
