A Review Paper on Multiple Face Detection Techniques

Multiple Face Recognition (MFR) has gained significant attention in recent years due to its applications in security, surveillance, and authentication systems. With advancements in deep learning, computer vision, and edge computing, MFR has seen notable improvements in accuracy and efficiency. Traditional face recognition methods relied on handcrafted feature extraction techniques such as Local Binary Patterns (LBP), Scale-Invariant Feature Transform (SIFT), and Principal Component Analysis (PCA). However, these approaches often struggled with challenges such as varying lighting conditions, occlusions, and pose variations. The introduction of deep learning, particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), has revolutionized face recognition by significantly improving accuracy and robustness.