# Fixing liveness and face match problems Source: https://help.didit.me/liveness-biometrics/liveness-and-face-match-issues Updated: 2026-10-12 Why liveness scores come back low, why face match sometimes fails or passes when it shouldn't, and what you can tune in your workflow. Separate two things first: did the **camera work**, and did the **check fail**? A camera that never opened is an environment problem (in-app browser, denied permission, camera in use). A check that failed is a threshold or capture-quality problem. [Video: Active vs passive liveness detection: which face check to choose - Didit Academy](https://www.youtube.com/watch?v=wGwcjNtvZ24) Liveness confirms a real person is in front of the camera; face match confirms that person is the one on the document. When either comes back with a low score or a warning, it's almost always one of a small set of causes. ## The camera never opened Before assuming the biometric check failed, rule this out - it produces the same "the selfie step didn't work" report from users: - **In-app browsers.** A verification link opened inside another app's built-in browser often can't get camera access at all. Ask the person to open the link in their phone's normal browser. - **Permission previously denied.** Once someone denies camera access for a site, the browser remembers and the flow can't ask again. They have to re-enable it in site settings. - **Camera already in use.** Another tab, app, or video call holding the camera blocks it. On some devices, a microphone permission request can also tie up the camera. - **No camera at all.** Desktop users without a webcam. Whether the selfie step is offered on desktop is a workflow setting. ## Liveness scored low or found no face - **No face detected** - poor lighting, the face out of frame, or a technical issue during capture. Ask the person to retake in better light with their face centered and nothing covering it. - **Low liveness score** - can be a genuine low-quality capture (bad lighting, low camera resolution) or a suspected spoof (photo, screen replay, mask). If you're seeing this on real users, request a resubmission rather than declining outright. - **Presentation attack detected** (`LIVENESS_FACE_ATTACK`) - the system found strong evidence of a spoof attempt. This one is intentional and auto-declines the session. ![A verification record in the Didit console with the Liveness section and the More actions menu](https://help.didit.me/screenshots/session-detail-light.webp) *Diagnose from the session: the capture first, then the timeline.* If low liveness scores cluster on particular devices, check which method your workflow uses: active methods (3D Flash, 3D Action) are noticeably harder on older phones and in dim rooms than passive liveness. See [how liveness detection works](https://help.didit.me/liveness-biometrics/how-liveness-works). ## Face match doesn't look right Face match compares the selfie against the photo on the document and produces a similarity score, checked against your workflow's thresholds: - **Score below the review threshold** - the session goes to **In review** for a human to confirm. - **Score below the decline threshold** - the session is declined automatically. - **No face found in the selfie or the document photo** - the person is normally prompted to retake the selfie (they get a few attempts) before this is treated as a final result. The score is also affected by things that have nothing to do with identity: an old document photo, a heavily worn card, glasses, a hat, a beard grown since the photo was taken, or a very low-resolution portrait on the document. Those all legitimately depress the score for the right person. If a genuine user is repeatedly failing face match, or you're seeing approvals you don't think should have passed, the fix is to adjust the review and decline thresholds. See [face match scores and thresholds](https://help.didit.me/liveness-biometrics/face-match-thresholds). ## A legitimate returning customer flagged as a duplicate The liveness step also runs a 1:N face search against everyone you've already verified. Your own repeat customers will match themselves - which is expected, not a fraud signal. Pass `vendor_data` consistently so both sessions resolve to the same person. See [duplicate accounts and face search](https://help.didit.me/verification-sessions/duplicate-detection). ## Reducing document capture fraud If you're seeing users submit a photo of a document instead of scanning the physical item live, or a third person capturing the ID for someone else, your workflow's ID Verification and liveness settings let you tighten this up: - Restrict capture to the **live camera only**, removing the upload path. - Use the **cross-camera face match** check, which compares the face seen by the camera during document capture against the selfie, and route mismatches to review or decline instead of passing them silently. > **Warning:** Removing the upload path raises assurance and lowers completion. Users on > locked-down devices, with a broken camera, or on a desktop with a poor webcam > will fail rather than fall back. Decide which matters more for the flow in > question rather than applying it everywhere. ## When it's the same person but a very old document For a document whose portrait is a decade old, no threshold setting is going to be reliably right. The better answer is a stronger check: NFC reads the portrait straight off the chip, which is the same image the issuer holds, and removes the print-quality variable entirely. See [NFC chip verification](https://help.didit.me/documents-coverage/nfc-chip-verification). ## Related - [Why a verification was declined](https://help.didit.me/verification-sessions/why-was-a-session-declined) - [Fixing document upload and reading problems](https://help.didit.me/documents-coverage/document-upload-problems) - [Letting someone try a verification again](https://help.didit.me/verification-sessions/retry-a-verification)