Face Recognition System

Face recognition systems aim to automatically identify individuals from their facial images, with applications ranging from security to personalized services. Current research emphasizes improving robustness against attacks like morphing (combining faces to deceive the system) and spoofing (using masks or other imitations), often employing deep learning models such as convolutional neural networks (CNNs) and Vision Transformers (ViTs), along with techniques like federated learning to address privacy concerns. Ongoing efforts focus on enhancing explainability, addressing algorithmic bias (particularly concerning ethnicity and gender), and optimizing systems for real-time performance and resource efficiency.