Examination of fingerprint separation methods based on hyperspectral data measured from latent overlapping fingerprints

Fingerprints provide important clues to criminal investigations. Although there are various fingerprint detection methods such as powder or liquid, optical methods are useful for non-contact and non-destructive detection. However, in case of two or more overlapping fingerprints, they might be discarded because the features cannot be assigned to the individual fingerprints. The fact that the composition of fingerprints is unique for each individual is well known, so if this causes differences in inherent emission spectra of fingerprints, it is possible to separate overlapping fingerprints. Hyperspectral imaging is used in a variety of fields and also in forensic science, such as fingerprint detection. In this study, the separation of overlapping fingerprints using multivariate analysis was performed for effective use of fingerprints. Fluorescence hyperspectral data of overlapping fingerprints excited by a 532 nm CW laser were acquired by hyperspectral imaging in the visible region. Fluorescence spectra from fingerprints were measured in the wavelength range from 560 to 700 nm with the wavelength resolution of 1.1 nm. Thus, the hyperspectral data cube consisted of 600 (image) x 960 (image) x 128 (wavelength) pixels. An image, which are integrated over the wavelength range, showed the two fingerprints overlapping each other. Separation of overlapping fingerprints was tried applying principal component analysis, multivariate curve resolution - alternating least squares analysis, and partial least squares analysis to the fluorescence hyperspectral data. Among three methods examined herein, partial least squares analysis was found to be most effective for fingerprint separation.