Automated Convict Identification Through Machine Learning-Based Facial Recognition

In a world riddled with criminal incidents, the swift identification of wrongdoers remains an ongoing challenge. Criminals have become adept at erasing biological and fingerprint traces from crime scenes, rendering traditional investigative methods ineffective. Modern facial identification technologies offer a rapid solution to this problem. With the widespread use of CCTV cameras, driven by security technology advancements, a unique opportunity arises. Our research seeks to automatically detect and identify convicts from video footage, providing a powerful tool for law enforcement. This technology extends to identifying suspects, criminals, missing persons, and more, saving valuable time and effort. Our study focuses on developing a machine learning-based criminal face detection system to streamline law enforcement by automating criminal identification. Employing a mixed methods approach involving data collection and focus group discussions, we gain insights from field practitioners. Our survey reveals a significant challenge in identifying culprits, with nearly 60% of cases analyzed yielding unidentified perpetrators. We propose a novel machine learning based automated method, which integrates with a comprehensive database containing individuals’ information and diverse images. Once identified, the system conducts real-time database comparisons, providing immediate results when a match is found. We explore techniques for criminal identification from photographs and introduce innovative ideas. Our research comprehensively covers various models, offering a promising solution to the challenges of locating and identifying convicts in modern law enforcement.