Face Recognition and Criminal Face Detection System

Face recognition technology has emerged as a powerful tool in security and surveillance systems. This literature survey focuses on face recognition and criminal detection systems using modern machine learning and deep learning techniques. The primary objective is to analyze various algorithms such as Haar Cascade, Eigenfaces, Fisherfaces, Local Binary Patterns (LBP), and Convolutional Neural Networks (CNN). This paper reviews multiple research works that highlight the effectiveness, accuracy, and limitations of these techniques in real-world applications. The study also explores how these systems are used in criminal identification by comparing captured facial images with stored databases. Challenges such as lighting variations, pose differences, and real-time processing are discussed. The survey concludes by identifying research gaps and suggesting future improvements for developing more efficient and accurate face recognition systems.