Published Paper Details:

In recent years, the process of identifying criminals has become more complex due to the evolving tactics used to evade capture. Traditional identification methods, such as fingerprinting and DNA analysis, are often slow and inefficient, especially when modern criminals consciously avoid leaving behind biological traces. To address this challenge, law enforcement agencies are increasingly adopting advanced technological tools like face recognition systems, which offer faster and more reliable methods for identifying suspects. This paper presents the development of a criminal identification system using face recognition technology, implemented in Python. The system harnesses existing surveillance infrastructure, such as CCTV cameras, and employs machine learning algorithms to detect and recognize faces in real-time. By integrating computer vision techniques with deep learning-based facial recognition models, the system automates the identification process, reducing the dependency on traditional methods. The proposed system is designed to work in diverse environmental conditions and can handle variations in lighting, facial expressions, and occlusions. Through extensive testing, the system has demonstrated a high level of accuracy and efficiency, making it a valuable tool for law enforcement agencies. This project aims to streamline criminal identification, ensuring faster suspect tracking, enhancing public safety, and improving response times. The paper further explores the potential challenges, including data privacy concerns, ethical implications, and system limitations, providing insights into how such technologies can be effectively integrated into modern law enforcement practices.