Potential Application of Machine Learning in Forensic Ballistics
Pooja Ahuja, Kanica Chugh, Niha Ansari · 2025
Machine-learning forensics (MLF) is an emerging field within forensic science that leverages machine learning to identify criminal patterns, predict criminal activities (e.g., predict the location and timing of crimes) and automate investigative processes. Forensic ballistics, a specialized discipline within forensic science, focuses on the examination and analysis of firearms, ammunition and associated ballistic evidence to aid criminal investigations. Its primary objective is to establish connections between firearms and specific criminal incidents. The forensic ballistics process involves meticulous examination of firearms, including documentation of make, model, serial number and any modifications. Similarly, ammunition is scrutinized for calibre, cartridge type and manufacturer-specific markings. The integration of machine learning in forensic ballistics holds significant potential for enhancing the efficiency and accuracy of analyses. Machine learning plays a crucial role in improving the accuracy and reliability of ballistic image matching, especially in operational forensic settings. The potential exists to develop robust and generalizable algorithms that will serve as a beneficial tool for forensic investigators in estimating shooting distances from shotgun patterns, particularly in scenarios with limited background information available.