DEEPFACE FACIAL RECOGNITION SYSTEM WITH LAW ENFORCEMENT APPLICATIONS

Abstract

Abstract— Deep learning-driven facial recognition technology has been a game-changer for law enforcement, improving both the precision and efficacy of person identification. DeepFace is unique among these technologies because of its extreme precision and resilience. This study examines the use of DeepFace in law enforcement for border security, real-time surveillance, suspect identification, and missing person searches. DeepFace achieves over 97% accuracy in face verification tasks by using sophisticated techniques including convolutional neural networks (CNNs) and 3D facial modeling. DeepFace can be implemented in real-world applications with the use of tools like Tensor-Flow, PyTorch, OpenCV, and specialized libraries like DeepFace in Python. In this area, DeepFace's future depends on ongoing algorithmic advancements, system integration with other biometric systems, and the creation of moral guidelines to guarantee appropriate usage. This has led to the adoption of facial recognition in areas such as security, business systems, academia and law enforcement. This research introduces a real-time face recognition system that uses deep learning, specifically DeepFace and also compares its accuracy to other algorithms. The system will perform a find function which establishes if a similar facial image exists in the database and then establishes the identity of the person by extracting facial features. The proposed system helps the law, with high accuracy in criminal identification. Law enforcement officials can respond the incidents swiftly and apprehend suspects since the face recognition technology uses high speed of processing through deep learning. A crime free society is what all communities seek to achieve, thus this system can go a long way in combating crime and providing safety in societies.