Hyperspectral Imaging for Bloodstain Identification

Abstract

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

Forensic applications of hyperspectral imaging technique: a narrative review

“…Oxyhemoglobin and methemoglobin perform characteristic peaks in the visible light spectrum, marked as b (542 nm) and a (576 nm), which detect blood and differentiate it from other substances. 5 Zulfiqar et al 6 proposed an HSI-based experimental protocol able to differentiate bloodstains from other substances similar in colour and consistency (ketchup, rust-coloured acrylic paint, red and brown acrylic paint, red and rust nail polish, fake blood and red ink) on different substrates (white cotton fabric, white tiles and PVC sheet); besides, the samples were examined for three days to investigate their behaviour over time. The authors concluded that the proposed method achieved the best results.…”

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

“…When comparing a Crime-lite ® ML2 ALS instrument (Foster and Freeman Ltd., Evesham, UK) and HIS-NIR imaging using an SWIR3 hyperspectral camera working in the 1000-2500 nm spectral range (Specim Ltd., Oulu, Finland) [66], the resultant images obtained from the HIS-NIR imaging were shown to be superior at revealing the location of body fluid staining in known reference samples, simulated casework samples and on unknown staining on forensic exhibits. Using blood samples and different substrates, spectra were generated [67] that were used to train different classifiers to detect aged bloodstains and most recently a "blood detection dataset" was produced [68] specifically to assist in the challenging task of algorithm development for this new technology.…”

Recent Advances in Multi- and Hyperspectral Image Analysis

“…The experiments showed that deep learning can indeed outperform classical machine learning in blood stain classification, especially in the more challenging multiple-image scenario. Blood identification was also tackled by Zulfiqar et al, and the authors suggested a new method based on the visualization of heme-components bands in the 500–700 nm spectral range [ 34 ]. In their algorithm, Savitzky Golay filtering is used to highlight the subtle bands of all samples.…”