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

*Corresponding author for this work

Research output: Contribution to journalComment/debatepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1208-1214
Number of pages7
JournalJournal of Forensic Sciences
Volume67
Issue number3
DOIs
Publication statusPublished - 2022 May

Keywords

  • fingermark
  • fingerprint
  • fluorescence
  • hyperspectral imaging
  • multivariate curve resolution–alternating least squares
  • principal component analysis

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Genetics

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