Eyeverify Inc.

Jumio Corporation

Casey Hughlett

Reza R Derakhshani

Vikas Gottemukkula

Joel Teply

Toby Rush

Jeremy Paben

Sashi Kanth Saripalle

Riddhiman Das

Greg Storm

Matthew Barrow

Zikomo Fields

Computer-implemented methods and systems for image security in computer vision applications are described. A shared secret is stored at an image sensor module and a trusted element. The shared secret includes a device-specific secret value and a definition of a plurality of pixel locations. An image is received at the image sensor module, which generates a watermark based on the secret value and applies the watermark to the image, based on the pixel locations, to create a watermarked image. The watermarked image is received at the trusted element, which validates the watermarked image based on the watermark.

Secure image pipeline

A biometric template created at a user device is divided into portions that are distributed among members of a trusted circle and, optionally, a remote storage service. When the user associated with the biometric template attempts to reauthenticate on a different user device, live identity information is captured and transmitted to trusted circle members. The members confirm the identity of the user and provide the biometric template portions to the different device for reconstruction of the original template. The user can then biometrically reauthenticate using the reconstructed template.

Verified and private portable identity

Methods and systems for securing biometric templates and generating secret keys are provided. One or more images are received. Interest points are identified based on the received images, and a plurality of obfuscating data points are generated based on the interest points. An obfuscated template based on the interest points and the obfuscating data points is created and stored. A secret key can be encoded using a subset of at least one of the obfuscating data points and the interest points in the template.

Biometric template security and key generation

This specification describes technologies relating to biometric authentication based on images of the eye. In general, one aspect can be embodied in methods that include obtaining one or more image regions from a first image of an eye. Each of the image regions may include a view of a respective portion of the white of the eye. The method may further include applying several distinct filters to each of the image regions to generate a plurality of respective descriptors for the region. The several distinct filters may include convolutional filters that are each configured to describe one or more aspects of an eye vasculature and in combination describe a visible eye vasculature in a feature space. A match score may be determined based on the generated descriptors and based on one or more descriptors associated with a second image of eye vasculature.

Texture features for biometric authentication

This specification describes technologies relating to biometric authentication based on images of the eye. In general, one aspect of the subject matter described in this specification can be embodied in methods that include obtaining images of a subject including a view of an eye. The methods may further include determining a behavioral metric based on detected movement of the eye as the eye appears in a plurality of the images, determining a spatial metric based on a distance from a sensor to a landmark that appears in a plurality of the images each having a different respective focus distance, and determining a reflectance metric based on detected changes in surface glare or specular reflection patterns on a surface of the eye. The methods may further include determining a score based on the behavioral, spatial, and reflectance metrics and rejecting or accepting the one or more images based on the score.

Spoof detection for biometric authentication

One aspect comprises obtaining one or more image regions from a first image of an eye. Each of the image regions may include a view of a respective portion of the white of the eye. The aspect may further comprise applying several distinct filters to each of the image regions to generate a plurality of respective descriptors for the region. The several distinct filters may include convolutional filters that are each configured to describe one or more aspects of an eye vasculature and in combination describe a visible eye vasculature in a feature space. A match score may be determined based on the generated descriptors and based on one or more descriptors associated with a second image of eye vasculature.

In general, one aspect of the subject matter described can be embodied in methods that include obtaining one or more image regions from a first image of an eye. Each of the image regions may include a view of a respective portion of the white of the eye. The method may further include applying several distinct filters to each of the image regions to generate a plurality of respective descriptors for the region. The several distinct filters may include convolutional filters that are each configured to describe one or more aspects of an eye vasculature and in combination describe a visible eye vasculature in a feature space. A match score may be determined based on the generated descriptors and based on one or more descriptors associated with a second image of eye vasculature.

This specification describes technologies relating to biometric authentication based on images of the eye. In general, one aspect of the subject matter described in this specification can be embodied in methods that include obtaining one or more image regions from a first image of an eye. Each of the image regions may include a view of a respective portion of the white of the eye. The method may further include applying several distinct filters to each of the image regions to generate a plurality of respective descriptors for the region. The several distinct filters may include convolutional filters that are each configured to describe one or more aspects of an eye vasculature and in combination describe a visible eye vasculature in a feature space. A match score may be determined based on the generated descriptors and based on one or more descriptors associated with a second image of eye vasculature.

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Lenexa, KS, United States of America

Casey Hughlett