US 11,768,926 B2Granted

Method of textured contact lens detection

AssigneeUniversity of Notre Dame du Lac

InventorsAdam Czajka, Kevin Bowyer, Zhaoyuan Fang

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Abstract

Systems and methods for detecting textured contact lenses in an iris image are disclosed. Initially, “K” images are taken of an eye in near-infrared light, each for a different positioning of the illuminant. Three-dimensional properties of the hypothetical iris are estimated based on image processing methods. The variability of the estimated three-dimensional properties is calculated, and a classification into two classes is made based on the variability. A low variability denotes that an authentic iris pattern was presented to the sensor, whereas a high variability denotes that the sensor observes an eye wearing a textured contact lens. The systems and methods disclosed allow for detecting presentation attacks to iris recognition systems/sensors based on presentation of an eye wearing a textured contact lens in an automatic and accurate way.

Claims18

Dependent claims

A computer-implemented method for determining a presence of a textured contact lens on an eye, comprising:

illuminating the eye with k illuminants, the k illuminants comprising at least two different illuminants placed at different angles relative to a camera;
acquiring iris images using the camera, the acquired iris images comprising at least two images acquired under illumination by each of the k illuminants;
and
determining, using one or more processors, the presence of a textured contact lens based on an analysis of an estimated surface shape of a selected portion of an iris determined within the acquired iris images, wherein determining the presence of a textured contact lens comprises:
calculating normal vectors to the observed surface based on the acquired iris images;
calculating a nonoccluded portion of the iris annulus based on the acquired iris images;
determining a variance of the normal vectors to the observed surface found within the nonoccluded portion of the iris annulus;
and
comparing the variance to an acceptance threshold and outputting a result indicating the presence of a textured contact lens if the variance exceeds the acceptance threshold.

6 more elements

A computer system for determining a presence of a textured contact lens on an eye, comprising:

one or more processors;
and
one or more storage devices having stored thereon computer-executable instructions that when executed by the one or more processors configure the computer system to perform at least the following:
receive a plurality of iris images, each iris image of the plurality of iris images being acquired during illumination with one of k illuminants, the k illuminants comprising at least two different illuminants placed at different angles relative to a camera used to acquire the plurality of iris images;
and
determine, using the one or more processors, the presence of a textured contact lens based on an analysis of an estimated surface shape of a nonoccluded portion of an iris determined within the plurality of iris images, wherein determining the presence of the textured contact lens comprises:
calculating normal vectors to an observed surface based on the plurality of iris images;
calculating a nonoccluded portion of an iris annulus based on the plurality of iris images;
determining a variance of the normal vectors to the observed surface found within the nonoccluded portion of the iris annulus;
and
comparing the variance to an acceptance threshold and outputting a result indicating the presence of a textured contact lens if the variance exceeds the acceptance threshold.

8 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/264,495Filed 24 Oct 2019 · us provisional application
Priority: 24 Oct 2018
Published: US 2021/0319239 A114 Oct 2021
Granted: 26 Sep 2023
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06F 21/32
User authentication: using biometric data, e.g. fingerprints, iris scans or voiceprints
G06T 7/11
Segmentation; Edge detection: region-based segmentation
G06T 7/586
From multiple images: from multiple light sources, e.g. photometric stereo
G06V 40/18
Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands: eye characteristics, e.g. of the iris