Face recognition technology identifies or verifies individuals by analyzing their facial features. It involves multiple steps: detection, feature extraction, and matching.
First, a camera captures an image or video frame, and the system uses algorithms to detect faces in the input. Modern systems often rely on deep learning-based methods like YOLO or Haar cascades for real-time detection.
Next, the system extracts facial features, such as the shape of the nose, distance between the eyes, and jawline structure. These features are encoded as embeddings—unique numerical representations generated by neural networks, such as convolutional neural networks (CNNs).
The embeddings are compared with entries in a database using similarity metrics like cosine similarity or Euclidean distance. If a match exceeds a predefined threshold, the identity is confirmed.
Face recognition technology is widely used in security (e.g., unlocking devices, surveillance), retail (personalized shopping), and healthcare (patient verification). While accurate, its performance can be affected by poor lighting, angles, or obstructions. Privacy and ethical concerns have also prompted regulations to ensure responsible usage.