From Images to Detection: Machine Learning for Blood Pattern Classification

TL;DR

This paper presents a machine learning approach to classify impact spatter and gunshot bloodstain patterns, improving accuracy and efficiency in bloodstain pattern analysis for crime scene reconstruction.

Contribution

It introduces a novel combination of feature extraction, data consolidation, and boosting classifiers specifically for bloodstain pattern differentiation.

Findings

Model achieves high accuracy in pattern classification

Effective feature extraction enhances classifier performance

Discusses challenges and future directions in BPA

Abstract

Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distribution. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, we have developed a model that excels in both accuracy and efficiency. In addition, we use outside data sources from previous studies to discuss the challenges and future directions for BPA.