Eyeverify Inc.

Jumio Corporation

Joel Teply

Reza R Derakhshani

Casey Hughlett

Toby Rush

Jeremy Paben

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

Spoof-detection and liveness analysis is performed using a software-based solution on a user device, such as a smartphone having a camera, audio output component (e.g., earpiece), and audio input component (e.g., microphone). One or more audio signals are emitted from the audio output component of the user device, reflect off a target, and are received back at the audio input component of the device. Based on the reflections, a determination is made as to whether the target is comprised of a three-dimensional face-like structure and/or face-like tissue. Using at least this determination, a finding is made as to whether the target is likely to be spoofed, rather than a legitimate, live person.

Systems and methods for spoof detection and liveness analysis

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.

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Shawnee, KS, United States of America

Joel Teply