AI-powered face recognition identifies or verifies individuals by analyzing their facial features using artificial intelligence. Unlike traditional methods, which rely on manual feature engineering, AI systems use machine learning models to automatically learn patterns from large datasets.
The process starts with detecting faces in an image or video feed using AI models like YOLO or MTCNN. Once detected, the system extracts features such as the distance between eyes or the shape of the mouth. These features are encoded into embeddings—unique numerical representations of the face.
The embeddings are compared with entries in a database to find a match. AI-powered systems often use deep learning models, like convolutional neural networks (CNNs), to achieve high accuracy in generating embeddings.
Applications range from unlocking devices and access control to surveillance and personalized shopping. AI-powered face recognition is particularly valuable in dynamic environments, where lighting, pose, and occlusions vary.
Advanced systems also integrate liveness detection to prevent spoofing with photos or videos. While highly effective, these systems raise privacy and ethical concerns, requiring developers to adhere to data protection regulations and implement robust security measures.