Liveness detection
Active and passive liveness checks, iBeta-certified at PAD Level 1 and Level 2 under ISO/IEC 30107-3.
Make sure a live person is behind every remote verification, not a photo, mask, replayed video, or AI-generated fake
Regula’s liveness detection SDK stops spoofing attempts while keeping remote verification fast and easy for genuine users
Detect spoofing attempts before access is granted
Confirm live presence in seconds without unnecessary friction
Integrate liveness detection across web and mobile or within your own infrastructure
PAD Level 1&2 ISO 30107-3
Top 3 FRVT age assurance
Privacy by default
ISO 9001
BSI TR-03105 Teil 5.1
BSI TR-03105 Teil 5.2
Regula’s liveness detection technology helps detect a wide range of presentation and injection attacks:
static images held up to the camera
video played from a phone or tablet
synthetic video fed directly into the camera stream
faces created or swapped using generative models
physical forgeries designed to fool depth sensors
Skip integration headaches and cut development time. With ready-to-run Linux, Windows, and Docker packages, clear docs, and code samples, you can launch faster—whether building a new app or enhancing an existing one.
Run a liveness check, test face matching with our samples or your own images, and review face image quality scores
The strongest proof comes from the people using the product. Explore how Regula performs across the metrics identity verification teams care about.
What is liveness detection and why does it matter?
Liveness detection determines whether the biometric sample comes from a live person present in front of the camera — not a photo, video, or mask. Without liveness detection, any face recognition system can be bypassed with a printed photo. It is a mandatory component of any compliant biometric identity verification flow.
What is the difference between active and passive liveness detection?
Active liveness detection asks the user to perform a specific action — blinking, turning their head, or following a visual prompt. Passive liveness detection works silently in the background, with no user action required. Passive detection reduces friction in the user experience while maintaining a strong security level. Regula Face SDK supports both, and the mode can be selected based on your risk model.
How does Regula’s liveness detection SDK work?
Regula’s liveness detection SDK analyzes facial and session signals to determine whether a live person is present. It supports active and passive checks and processes the session on the server, helping detect photos, masks, replays, and injected media without relying solely on results from the user’s device.
What is the difference between face verification and face identification?
Face verification (1:1) checks whether two images show the same person — for example, a selfie versus a passport portrait. Face identification (1:N) searches a database to find who a person is, or to check whether they appear in a watchlist. Regula Face SDK supports both modes.
How does Regula Face SDK protect against deepfake attacks?
The liveness detection engine in Regula Face SDK is trained on a dataset that includes AI-generated and deepfake attack samples. It analyzes texture, micro-movement patterns, and reflection artifacts that are absent in synthetic faces. The model is updated continuously as new attack techniques emerge.
Does face liveness verification require users to perform any actions?
Not necessarily. Passive liveness works without gestures or additional instructions, the user simply looks at the camera. Active liveness asks the user to follow simple on-screen prompts when additional assurance is required.
How can Regula’s liveness detection SDK be integrated into an existing verification flow?
Regula Face SDK supports web and mobile applications and server-side integration. Liveness detection can be added as a standalone check or combined with face matching and other biometric checks, with cloud and on-premises deployment options available.