Methods and systems for detecting fraud during biometric identity verification

Patent metadata

Field	Value
Publication number	US-12562007-B2
Application number	US-202318212797-A
Country	US
Kind code	B2
Filing date	Jun 22, 2023
Priority date	Jun 27, 2022
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

Described herein are computerized methods and systems for detecting fraud during biometric identity verification. A mobile device captures video comprising a plurality of frames of a person's face. The mobile device extracts from the video frames comprising at least two frames of the person's face from different angles. The mobile device creates a reconstruction of the person's face using the extracted frames and derives signals associated with features of the person's face. The mobile device generates an embedding for each extracted frame using the extracted frames, the three-dimensional reconstruction, and the signals. The mobile device calculates, for each extracted frame, a fraud confidence value based upon the embedding for the extracted frame, attributes of the person's face, and image quality attributes of the extracted image. The mobile device computes a fraud detection decision for the extracted frames based upon the fraud confidence values and the embeddings.

First claim

What is claimed is:

1 . A computer system for detecting fraud during biometric identity verification, the system comprising a mobile computing device coupled to an image capture device and a server computing device, the mobile computing device configured to:
capture, using the image capture device, video comprising a plurality of frames of a person's face; extract, from the video, at least two frames of the person's face from each of a plurality of different angles; create a three-dimensional reconstruction of the person's face using the extracted frames; derive one or more signals associated with features of the person's face using the extracted frames; and the server computing device configured to: generate an embedding for each extracted frame using the extracted frames, the three-dimensional reconstruction, and the derived signals; calculate, for each extracted frame, a fraud confidence value based upon the embedding for the extracted frame, one or more attributes of the person's face, and one or more image quality attributes of the extracted image; and compute a fraud detection decision for one or more of the extracted frames based upon the fraud confidence values and the embeddings.

2 . The system of claim 1 , wherein, during capture of the video, the mobile computing device generates instructions for movement of the person's head in one or more directions relative to the image capture device.

3 . The system of claim 2 , wherein the instructions comprise a user interface for display to the person, the user interface comprising:
a view of the person's face, a first visual indicator on a left side of the view of the person's face, and a second visual indicator on a right side of the view of the person's face, wherein one or more appearance characteristics of the first visual indicator change as the person's head rotates to the right, and wherein one or more appearance characteristics of the second visual indicator change as the person's head rotates to the left.

4 . The system of claim 3 , wherein the first visual indicator and the second visual indicator each comprises a line.

5 . The system of claim 4 , wherein:
one or more of a color or a thickness of at least a portion of the line for the first visual indicator changes as the person's head rotates to the right, and one or more of a color or a thickness of at least a portion of the line for the second visual indicator changes as the person's head rotates to the left.

6 . The system of claim 5 , wherein:
the entire line for the first visual indicator changes color and thickness when the person's head has rotated a minimum number of degrees to the right, and the entire line for the second visual indicator changes color and thickness when the person's head has rotated a minimum number of degrees to the left.

7 . The system of claim 6 , wherein the mobile computing device generates one or more of an audible alert or a haptic alert when the person's head has rotated a minimum number of degrees to the right and a minimum number of degrees to the left during capture of the video.

8 . The system of claim 6 , wherein the mobile computing device stops capturing the video when the person's head has rotated a minimum number of degrees to the right and a minimum number of degrees to the left.

9 . The system of claim 8 , wherein the mobile computing device determines that the captured video comprises enough frames to enable the server computing device to compute a fraud detection decision prior to stopping the video capture.

10 . The system of claim 8 , wherein the mobile computing device restarts the video capture when the mobile computing device determines that the captured video does not comprise enough frames to enable the server computing device to compute a fraud detection decision.

11 . The system of claim 1 , wherein creating a three-dimensional reconstruction of the person's face using the extracted frames comprises:
generating a plurality of keypoints associated with one or more geometrical locations on the person's face from each extracted frame, wherein the geometrical locations are associated with higher levels of 3D variation; for each keypoint: locating the keypoint in each extracted frame, and determining a relationship between the locations of the keypoint across the extracted frames; generating a point cloud based upon the keypoints; and selecting a plurality of camera positions based upon the relationships between the locations of the keypoints across the extracted frames, wherein for each extracted frame, an orientation of the camera is biased toward a face orientation.

12 . The system of claim 11 , wherein the camera positions are selected based upon a minimized reprojection error for the associated keypoints.

13 . The system of claim 11 , wherein generating a plurality of keypoints associated with one or more geometrical locations on the person's face from each extracted frame comprises:
identifying one or more facial landmarks of the person's face, the facial landmarks collectively covering the full face; and generating one or more keypoints corresponding to each of the facial landmarks.

14 . The system of claim 13 , wherein the mobile computing device removes one or more keypoints that are located outside of the person's face.

15 . The system of claim 1 , wherein deriving one or more signals associated with features of the person's face using the extracted frames comprises:
determining one or more features of the person's face using the extracted frames; and deriving the one or more signals based upon the determined features of the person's face.

16 . The system of claim 15 , wherein the one or more features of the person's face comprise contour lines of the person's face, regions of the person's face, or both.

17 . The system of claim 1 , wherein generating an embedding for each extracted frame using the extracted frames, the three-dimensional reconstruction, and the derived signals comprises, for each extracted frame:
executing a machine learning classification model using the extracted frame, the three-dimensional reconstruction, and the derived signals as input to generate the embedding.

18 . The system of claim 17 , wherein the machine learning classification model comprises an ensemble of supervised and unsupervised neural networks.

19 . The system of claim 1 , wherein the one or more attributes of the person's face for calculating the confidence value comprise face pose, face size, and face detection confidence.

20 . The system of claim 1 , wherein the one or more image quality attributes of the extracted image comprise image corrections, white balance, brightness, blurriness, and contrast.

21 . The system of claim 1 , wherein the fraud confidence values associated with each extracted frame are aggregated to generate an overall fraud confidence value.

22 . The system of claim 21 , wherein computing a fraud detection decision based upon the fraud confidence values for one or more of the extracted frames comprises:
comparing the overall fraud confidence value to a fraud confidence threshold; determining that the person in the video is genuine when the overall fraud confidence value falls below the fraud confidence threshold; and determining that the person in the video is fraudulent when the overall fraud confidence value meets or exceeds the fraud confidence threshold.

23 . The system of claim

Assignees

• Onfido Ltd
• Entrust Corp

Inventors

• Bruveris Martins
• Mahadevan Mohan
• Sabathe Romain
• Ehrhardt Sebastien
• Richard theo
• Opland Mark

Classifications

• G06F21/32

  using biometric data, e.g. fingerprints, iris scans or voiceprints · CPC title

• G06V40/166

  using acquisition arrangements · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V40/172

  Classification, e.g. identification · CPC title

• G06V40/165

  using facial parts and geometric relationships · CPC title