Liveness Verification

The Liveness Verification module determines whether a real, physically present person was in front of the camera at the point the selfie photograph was taken. It detects presentation attacks such as:

• Attempts to spoof the verification process using a printed photograph
• A screen replay
• A video recording
• A 3D mask

The module returns a confidence score indicating the likelihood that the captured selfie represents a genuine, live person.

This page contains documentation for the Liveness Verification module, including its capabilities and the scoring thresholds it uses.

Liveness Verification

The Liveness Verification module analyses a selfie image captured during the onboarding journey and produces a score that reflects the system’s confidence in the subject’s genuine presence.

It’s typically placed alongside or before the Facematch Verification module, which compares the selfie against the photograph on the subject’s identity document. Liveness verification confirms the person is real and present; facematch verification confirms they are the same person shown on the document.

Liveness verification is also supported as a standalone module. It can run on its own in a journey without requiring a Facematch Verification or document module to precede it. This is useful for use cases where confirming genuine human presence is the only check needed.

Capabilities

The module returns a single capability: a liveness score.

Liveness score

This capability returns an integer score from 0 to 100 indicating the system’s confidence that the selfie was captured from a real, physically present person at the time of the photograph.

Detail	Description
Type	Integer
Range	0–100
Default	0

A higher score indicates greater confidence that the selfie is genuine. A lower score indicates a higher likelihood that the image is a presentation attack or does not represent a live person.

The score reflects the combined output of multiple analysis signals, which may include

• Texture analysis: Detecting the flat, reflective surface of a printed photo or screen.
• Depth estimation: Distinguishing a three-dimensional face from a two-dimensional image.
• Edge and boundary detection: Identifying the borders of a held-up photograph or device screen.

A score of 0 indicates that the liveness check could not produce a result. This may occur if the selfie image was corrupted, if no face was detected in the image, or if a system error prevented analysis. Evaluation rules should treat a score of 0 as an inconclusive result rather than a definitive failure.

Default outcomes

The module uses score thresholds to classify the liveness result into one of three outcomes. These thresholds are pre-configured as default outcomes and can be used in evaluation and routing logic within the journey designer.

Outcome	Condition	Description
Success	Score ≥ 75	The system has high confidence that a real, live person was present when the selfie was captured. The journey can proceed to downstream biometric checks such as facematch verification.
Review	Score ≥ 60 and < 75	The system’s confidence is moderate. The selfie may be genuine, but the analysis detected some signals that could indicate a presentation attack, or the image quality reduced the system’s ability to make a definitive determination. Common causes include poor lighting, extreme camera angles, or partial face occlusion. This outcome typically routes to manual review, where a human operator can visually inspect the selfie and the surrounding session data.
Fail	Score < 60	The system has low confidence in genuine presence. The selfie is likely a presentation attack, a photograph of a photograph, a screen replay, a mask, or a digitally injected image. This outcome typically routes to rejection or escalation, depending on your fraud policy.
ERROR	Default (no conditions matched)	The liveness check could not be completed. This fallback outcome typically indicates a system-level issue, such as a service outage or image processing failure, rather than a spoofing attempt. For updates on status, check the GBG system status page.

Input payload

The following is a sample payload used to submit a selfie image to the Liveness Verification module for processing.

JSON

{
  "resourceId": "<resourceId>",
  "context": {
    "subject": {
      "biometrics": [
        {
          "faceImage": "<base64-encoded string>"
        }
      ]
    }
  }
}

Field	Required	Description
faceImage	Yes	Base64-encoded selfie image captured from the end user.
biometrics	Yes	Accepts at least one item.

Sample response

The following is a sample response for a successful liveness check from the Liveness Verification module.

JSON

{
  "response": {
    "advice": {
      "contains": {
        "score": 98
      }
    },
    "outcome": "Success"
  }
}