Hyperspectral Imaging for Bloodstain Identification

被引:33
作者
Zulfiqar, Maheen [1 ]
Ahmad, Muhammad [2 ,3 ]
Sohaib, Ahmed [1 ]
Mazzara, Manuel [4 ]
Distefano, Salvatore [3 ]
机构
[1] Khwaja Fareed Univ Engn & Informat Technol, Dept Comp Engn, Rahim Yar Khan 64200, Pakistan
[2] Natl Univ Comp & Emerging Sci, Dept Comp Sci, Chiniot Faisalabad Campus, Islamabad 35400, Chiniot, Pakistan
[3] Univ Messina, Dipartimento Matemat & Informat MIFT, I-98121 Messina, Italy
[4] Innopolis Univ, Inst Software Dev & Engn, Innopolis 420500, Russia
关键词
hyperspectral imaging; bloodstains identification; weak bands; SVM; ANNs; AGE ESTIMATION; INFRARED-SPECTROSCOPY; NONCONTACT DETECTION; STAINS; LUMINOL; CLASSIFICATION; VISUALIZATION; TRENDS;
D O I
10.3390/s21093045
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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 x 512 x 224, in which 1000 x 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.
引用
收藏
页数:20
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