Experimental study on the identification of bloodstain species based on hyperspectral imaging technology and machine learning methods

Sun Wei; Chen Ruili; Wang Haoyu; Sun Zhiwei; Han Xun

doi:10.1117/12.2681365

8 June 2023 Experimental study on the identification of bloodstain species based on hyperspectral imaging technology and machine learning methods

Sun Wei, Chen Ruili, Wang Haoyu, Sun Zhiwei, Han Xun

Author Affiliations +

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071C (2023) https://doi.org/10.1117/12.2681365
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Bloodstains, as a biological test material with a high occurrence rate at the scene of a criminal case, can provide a great deal of clue to the case. How to quickly, accurately and non-destructively distinguish between bloodstains and suspected bloodstains in a crime scene and identify the species of bloodstains is not only necessary for the examination of physical evidence in judicial practice, but also an inevitable requirement for the public security authorities to identify the case and reveal the falsified scene. Using a hyperspectral imager, we acquired hyperspectral data from 26 experimental materials, including 13 animal bloodstains and 13 suspected bloodstain compounds. This paper focuses on the identification of bloodstain species. The experimental technique builds a classification model for the identification of bloodstain species using three algorithms: K-nearest neighbor, Support Vector Machine, and Naive Bayes classification. The experimental results showed that the Naive Bayes classification algorithm and the Support Vector Machine classification model with RBF kernel function and Sigmoid as kernel function were the most efficient. Experiment classification accuracyof100.00% was achieved in all cases. Our experiments explore a new method for identifying different species of bloodstains using hyperspectral imaging technology.

Citation Download Citation

Sun Wei, Chen Ruili, Wang Haoyu, Sun Zhiwei, and Han Xun "Experimental study on the identification of bloodstain species based on hyperspectral imaging technology and machine learning methods", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071C (8 June 2023); https://doi.org/10.1117/12.2681365

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KEYWORDS

Blood

Biological samples

Hyperspectral imaging

Animals

Support vector machines

Education and training

Data modeling

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