Face recognition involves four major technical processes: image acquisition, face detection, feature extraction, and face matching.
Image Acquisition: This is the first step where a camera or sensor captures the image or video containing the face. The quality of the image significantly affects recognition accuracy. For example, a high-resolution frontal face image works better than a blurry side-profile shot.
Face Detection: In this step, the system locates and isolates the face within the captured image. It distinguishes the face from the background and other objects. For instance, algorithms like Haar cascades or deep learning-based models (e.g., MTCNN) can detect faces even in crowded scenes.
Feature Extraction: Once the face is detected, unique facial features (such as eye spacing, nose shape, and jawline) are extracted and converted into mathematical representations (feature vectors). Techniques like deep convolutional neural networks (CNNs) are commonly used. For example, a face might be represented as a 128-dimensional vector for comparison.
Face Matching: The extracted features are compared with stored face templates in a database to find a match. This is done by calculating the similarity (e.g., using Euclidean distance or cosine similarity) between the input face vector and stored vectors. For example, in access control systems, a scanned face is matched against enrolled employee faces to grant or deny entry.
In cloud-based applications, Tencent Cloud offers Face Recognition services that streamline these processes, providing APIs for real-time face detection, feature extraction, and matching with high accuracy and scalability.