A first step towards a machine learning-based framework for bloodstain classification in forensic science

Bloodstains found at a crime scene can help estimate the events that occurred during the crime. Reconstructing the crime scene by analyzing the bloodstain pattern contributes to understanding the bloody event. Therefore, it is essential to classify bloodstains through bloodstain pattern analysis (BPA) and accurately estimate the actions that took place at that time. In this study, we investigate the potential of using machine learning and deep learning to determine an action related to bloodstain data through the accessment of the corresponding bloodstain type by creating a prototype classification model. There are 14 types of bloodstain according to the classification system based on appearance. In this study, we test the classification potential of each bloodstain data for three bloodstain patterns such as Swing, Cessation, and Impact. Through experiments, it is shown that our prototype classification model for the selected bloodstains is developed and the accuracy of the resulting model is evaluated to be 80 %.