From Images to Detection: Machine Learning for Blood Pattern Classification
This work addresses a specific challenge in forensic science for crime scene investigators, but it appears incremental as it builds on existing methods for feature extraction and classification.
The study tackled the problem of distinguishing impact spatter from gunshot bloodstain patterns in Bloodstain Pattern Analysis (BPA) by extracting stain features and using boosting classifiers, resulting in a model that excels in accuracy and efficiency.
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.