Separation of overlapping fingerprints by principal component analysis and multivariate curve resolution–alternating least squares analysis of hyperspectral imaging data

Norimitsu Akiba*, Atsushi Nakamura, Takayuki Sota, Kazuhito Hibino, Hidetoshi Kakuda, Maurice C.G. Aalders

*この研究の対応する著者

研究成果: Comment/debate査読

抄録

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.

本文言語English
ページ(範囲)1208-1214
ページ数7
ジャーナルJournal of Forensic Sciences
67
3
DOI
出版ステータスPublished - 2022 5月

ASJC Scopus subject areas

  • 病理学および法医学
  • 遺伝学

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