ID verification with a mobile device
A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document and using automated verification of identification documents. Identification documents may be authenticated by validating security features. The system may determine features expected in a valid identification document and determine whether those features are present, employing techniques, such as determining whether direction-sensitive features are present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks.
1. At least one non-transitory computer-readable medium comprising computer-executable instructions which, when executed by a computing device, cause the computing device to carry out a method, the method comprising:
capturing a plurality of images using a camera; and
analyzing the captured plurality of images by:
recognizing images of the plurality of images as images of an identification document;
computing an angle of the camera relative to the identification document for the recognized images of the plurality of images;
identifying at least one security feature of the identification document based on the recognized images taken at multiple angles; and
verifying validity of the at least one security feature based on the recognized images taken at multiple angles.
2. The at least one non-transitory computer-readable medium of claim 1 , wherein capturing a plurality of images using a camera comprises:
providing instructions to a user indicating at what angles the camera of the computing device should be held relative to the identification document.
3. The at least one non-transitory computer-readable medium of claim 1 , wherein recognizing images of the plurality of images as images of an identification document comprises:
processing images of the captured plurality of images captured by identifying regions of the images comprising an identification document; and
comparing identified regions of the images to images of known identification documents.
4. The at least one non-transitory computer-readable medium of claim 3 , wherein computing the angle of the camera relative to the identification document comprises:
determining locations of marker features of the identification document based on images of known identification documents; and
projecting the marker features of the identification document onto an image plane of the camera for each image of the plurality of images.
5. The at least one non-transitory computer-readable medium of claim 3 , wherein recognizing images of the plurality of images as images of an identification document further comprises:
identifying a least one security feature as a hologram based on the images of known identification documents.
6. The at least one non-transitory computer-readable medium of claim 5 , wherein verifying validity of the at least one security feature comprises:
comparing an intensity value of each pixel of the identified regions with an intensity value of each pixel of an identified region of a mean image, the mean image being an image formed by averaging the plurality of images.
7. The at least one non-transitory computer-readable medium of claim 3 , wherein recognizing images of the plurality of images as images of an identification document further comprises:
identifying at least one security feature as being a lenticular feature comprising at least one of a lenticular image and lenticular text.
8. The at least one non-transitory computer-readable medium of claim 7 , wherein verifying validity of the at least one security feature comprises:
using template matching methods to determine validity of the lenticular image.
9. The at least one non-transitory computer-readable medium of claim 7 , wherein verifying validity of the at least one security feature comprises:
identifying a region around the lenticular text;
processing the region of at least one of the images of the plurality of images using binarization methods; and
extracting text from at least one processed region.
10. A computing device, comprising:
a camera;
at least one processor; and
at least one non-transitory computer-readable medium comprising instructions which, when executed by the at least one processor, cause the computing device to perform a method of:
capturing a plurality of images using the camera; and
analyzing the captured plurality of images by:
recognizing images of the plurality of images as images of an identification document;
computing an angle of the camera relative to the identification document for the recognized images of the plurality of images;
identifying at least one security feature of the identification document based on the recognized images taken at multiple angles; and
verifying validity of the at least one security feature based on the recognized images taken at multiple angles.
11. The computing device of claim 10 , wherein capturing a plurality of images using a camera comprises:
providing instructions to a user indicating at what angles the camera of the computing device should be held relative to the identification document.
12. The computing device of claim 10 , wherein recognizing images of the plurality of images as images of an identification document comprises:
providing instructions to a user indicating at what angles the camera of the computing device should be held relative to the identification document;
processing images of the plurality of images captured by the user by identifying regions of the images comprising an identification document; and
comparing the identified regions of the images to images of known identification documents.
13. The computing device of claim 12 , wherein the images of known identification documents are stored remotely.
14. The computing device of claim 12 , wherein computing the angle of the camera relative to the identification document comprises:
determining locations of marker features of the identification document based on the images of known identification documents; and
projecting the marker features of the identification document onto an image plane of the camera for each image of the plurality of images.
15. The computing device of claim 14 , wherein determining the angle of the image plane of the camera relative to the plane of the identification document further comprises:
determining a projection of pre-defined marker features of the identification document onto the image plane of the camera.
16. The computing device of claim 12 , wherein recognizing images of the plurality of images of an identification document further comprises:
identifying a least one security feature as a hologram based on the images of known identification documents.
17. The computing device of claim 16 , wherein verifying validity of the at least one security feature comprises:
comparing an intensity value of each pixel of the identified regions with an intensity value of each pixel of an identified region of a mean image, the mean image being an image formed by averaging the plurality of images.
18. The computing device of claim 12 , wherein recognizing images of the plurality of images of as images of an identification document further comprises:
identifying at least one of the security features as being a lenticular feature comprising at least one of a lenticular image and lenticular text.
19. The computing device of claim 18 , wherein verifying validity of the at least one security feature further comprises:
identifying a region around the lenticular text;
processing the region of at least one of the images of the plurality of images using binarization methods; and
extracting text from at least one processed region.
20. The computing device of claim 10 , wherein the method further comprises using the at least one processor to identify a plurality of images as comprising at least one of images of a live user and images of a spoof attack.
21. The computing device of claim 20 , wherein identifying a plurality of images as comprising at least one of images of a live user and images of a spoof attack comprises:
accessing a plurality of images comprising a face obtained by a camera;
providing the plurality of images to a trained deep learning model to obtain output indicating one or more likelihoods that the plurality of images comprise images of a live user and one or more likelihoods that the plurality of images comprise images of a spoof attack; and
identifying the plurality of images as comprising at least one of a live user and a spoof attack based on the output obtained from the trained deep learning model.
22. The computing device of claim 21 , wherein the trained deep learning model comprises at least one convolutional neural network and at least one feedback network.
23. The computing device of claim 22 , wherein the trained deep learning model is trained based on any one of:
training data built from facial depth features created by a three-dimensional morphable face model;
training data built from facial feature locations; and
feedback from the at least one feedback network.
24. The computing device of claim 21 , wherein the trained deep learning model is configured to identify spoof attacks including pre-recorded videos comprising a face, still images comprising a face, and live users wearing a mask.
25. The at least one non-transitory computer-readable medium of claim 4 , wherein computing the angle of the camera relative to the identification document further comprises determining a transformation matrix describing a transformation between a coordinate system of the identification document and a coordinate system of the camera.
26. The at least one non-transitory computer-readable medium of claim 1 , further comprising determining, based on the computed angle of the camera relative to the identification document, whether images of the recognized images were captured at suitably different angles to enable verification of the at least one security feature.