New Delhi, May 12 — The Delhi Police is set to introduce a cutting-edge 'reverse image search' software to enhance its criminal identification process by matching suspect sketches with a vast database of known offenders' photographs.
This advanced facial image reconstruction software is being developed by the Indraprastha Institute of Information Technology and is designed to significantly streamline the process of identifying suspects, especially when only a sketch based on eyewitness accounts is available.
AI-Powered Breakthrough in Law Enforcement
Until now, police personnel relied on manual comparison methods, which were time-consuming and often lacked precision. With the new software, suspect sketches can be digitally scanned and matched against criminal records using artificial intelligence and machine learning algorithms.“The software automates the comparison process, enabling real-time identification by filtering through police databases and offering a shortlist of likely matches,” said a senior police official.
Boost to Investigations in Serious Crimes
The system is expected to prove invaluable in high-priority investigations, such as murder, robbery, and sexual assault, where visual evidence may be sparse. By matching sketches with stored images even in the absence of CCTV footage, the tool could offer crucial breakthroughs in otherwise stalled cases.“This tool could be a game changer in solving cases where leads are limited,” the officer emphasized.
Part of a Larger Tech Push by Delhi Police
The reverse image software joins a growing arsenal of forensic and digital tools already in use by the Delhi Police. These include advanced data recovery systems capable of retrieving information from damaged storage devices and mobile phones, which are frequently used in both cybercrime and conventional crime investigations.With the integration of AI-driven technologies, Delhi Police aims to boost the accuracy, speed, and effectiveness of its criminal investigations, reflecting a broader push towards smarter and more data-centric policing.