Criminal Face Recognition System Using Artificial Intelligence

Abstract

This Project, Titled "Criminal Face Recognition System Using AI", Proposes An Intelligent System Designed To Identify And Track Individuals With Criminal Records Through Realtime Facial Recognition, Thereby Aiding Law Enforcement Agencies In Ensuring Public Safety. By Utilizing Deep Learning Techniques, Computer Vision, And A Robust Image-processing Pipeline, This System Can Automatically Detect Faces From Video Feeds Or Still Images And Match Them Against A Criminal Database With High Accuracy And Minimal False Positives. The Core Of This System Is Based On Convolutional Neural Networks (CNNs), Particularly Leveraging Pre-trained Models Like FaceNet, VGG-Face, Or Dlib For Efficient Facial Feature Extraction And Recognition. These Models Are Fine-tuned Using A Curated Dataset Of Criminal Mugshots To Increase Their Specificity In Recognizing Known Offenders. The Facial Recognition Pipeline Involves Four Key Stages: Face Detection, Face Alignment, Feature Extraction, And Face Matching. The System Also Incorporates OpenCV For Image Processing And TensorFlow/Keras Or PyTorch For Implementing Deep Learning Algorithms. To Make The Solution Scalable And Usable In Real-world Scenarios, A Cloud-based Database Is Integrated To Store And Manage Criminal Face Records Whenever A Face Is Detected By The Camera, The System Compares It With The Stored Records In Real-time. If A Match Is Found, Alerts Are Triggered To Notify Authorities With The Person’s Identification And Location

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