Facial Recognition System for Law Enforcement: An Integrated Approach Using Haar Cascade Classifier and LBPH Algorithm

Face recognition is an area of computer vision and image processing that is quickly expanding, with many uses in security, surveillance, and biometric identity. The proposed model is to develop a criminal identification system based on face recognition using OpenCV, Haar cascade classifier, LBPH, and AdaBoost algorithm. The problem that this project aims to address is the identification and tracking of criminals, which is a crucial duty for law enforcement organisations. The suggested technology is capable of real-time facial recognition and detection of criminals, which is achieved through the use of face recognition and facial detection methods based on machine learning. The system can register criminals and manage their data through a dataset, enabling the tracking and identification of criminals through CCTV footage or manually provided images. Compared to existing technologies, the proposed system is faster, more accurate, robust, reliable, and easy to use. The usage of machine learning-based methods for identifying and recognising faces enables a more accurate and efficient criminal identification system, which is critical in today's world.