Face recognition technology is driven by four core steps: face detection, face alignment, template extraction, and face matching. Let's explore how they work together.
Face DetectionFace detection finds and marks human faces in images or videos by drawing bounding boxes around them. Modern face recognition systems use AI and machine learning (like
Convolutional Neural Networks) to detect faces even in tough conditions such as bad lighting or different angles.
Face AlignmentAfter detection, the face is adjusted to a standard frontal position by using key facial landmarks (eyes, nose, mouth) to correct scale, rotation, and perspective differences. This makes the face look consistent for better accuracy in later steps.
Biometric Template ExtractionTemplate extraction converts the aligned face into a mathematical model that captures its unique features, such as shapes, textures, and patterns.
Advanced algorithms create a digital code that represents the face, allowing easy comparison with other templates.
Face Matching (1:1 and 1:N)- 1:1 Face Matching (Face verification) compares the face template with one reference template to confirm identity, like unlocking a phone.
- 1:N Face Matching (Face identification) compares the face template with many others in a database to identify someone, often used in security and surveillance to find people in a crowd.