Liveness detection detects spoofing attempts by identifying the characteristics of a live person. This technique makes heavy use of machine-learning algorithms and sensors to identify live biometric features such as faces, fingerprints, and voices.

In facial recognition systems, liveness detection algorithms will analyse subtle facial movements like blinking and head rotation to differentiate live faces from spoofed ones.

To detect facial presentation attacks, liveness detection uses 2D and 3D facial recognition technology, motion tracking, and thermal imaging techniques to scrutinise discrepancies in a person’s face.

There are two types of liveness detection:

1. Active liveness detection: This technique requires users to perform specific facial movements. These include actions such as smiling or blinking to prove their liveness.
2. Passive Liveness Detection: This technique does not require any input from the user. Instead, it analyses a subject’s face in real-time. It uses artificial intelligence and deep learning algorithms to identify spoofing attempts. Passive liveness checks happen in the background. For instance, a phone recognition system scans the user's face and detects natural movements like blinking to confirm authenticity.