What is the principle of liveness detection technology in face recognition?

Liveness detection technology in face recognition is designed to distinguish between a real, live human face and a fake one, such as a photo, video, or 3D mask. The core principle is to analyze dynamic or physiological characteristics that are difficult for forgeries to replicate.

There are two main approaches:

1. Active Liveness Detection: Requires user interaction, such as blinking, turning the head, or following on-screen instructions (e.g., "smile" or "move your head left"). The system verifies liveness by detecting these intentional movements.

   • Example: A face recognition system asks the user to blink twice within 5 seconds. A static photo cannot comply, but a live person can.

2. Passive Liveness Detection: Works without user cooperation by analyzing subtle cues like texture, reflections, micro-movements, or depth information. It uses algorithms to detect unnatural patterns in the image.

   • Example: The system checks for inconsistent lighting reflections on the eyes or skin texture that differs from real human skin.

In cloud-based face recognition solutions (e.g., Tencent Cloud Face Recognition), liveness detection is often integrated to enhance security for identity verification, preventing spoofing attacks. Tencent Cloud provides APIs that combine face detection with liveness checks to ensure only genuine users are authenticated.