Identification of Bloodstains by Species Using Extreme Learning Machine and Hyperspectral Imaging Technology

Abstract

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

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Keywords

Bloodstain species extreme learning machine hyperspectral imaging non-destructive

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