Magic Leap, Inc.

Alexey Spizhevoy

Adrian Kaehler

Vijay Badrinarayanan

Gary R Bradski

Gholamreza Amayeh

Systems and methods for eye image segmentation and image quality estimation are disclosed. In one aspect, after receiving an eye image, a device such as an augmented reality device can process the eye image using a convolutional neural network with a merged architecture to generate both a segmented eye image and a quality estimation of the eye image. The segmented eye image can include a background region, a sclera region, an iris region, or a pupil region. In another aspect, a convolutional neural network with a merged architecture can be trained for eye image segmentation and image quality estimation. In yet another aspect, the device can use the segmented eye image to determine eye contours such as a pupil contour and an iris contour. The device can use the eye contours to create a polar image of the iris region for computing an iris code or biometric authentication.

Neural network for eye image segmentation and image quality estimation

Systems and methods for iris authentication are disclosed. In one aspect, a deep neural network (DNN) with a triplet network architecture can be trained to learn an embedding (e.g., another DNN) that maps from the higher dimensional eye image space to a lower dimensional embedding space. The DNN can be trained with segmented iris images or images of the periocular region of the eye (including the eye and portions around the eye such as eyelids, eyebrows, eyelashes, and skin surrounding the eye). With the triplet network architecture, an embedding space representation (ESR) of a person's eye image can be closer to the ESRs of the person's other eye images than it is to the ESR of another person's eye image. In another aspect, to authenticate a user as an authorized user, an ESR of the user's eye image can be sufficiently close to an ESR of the authorized user's eye image.

Deep neural network for iris identification

Examples of systems and methods for determining a limbic boundary of an iris of an eye are provided. Properties of the corneal bulge can be computed from eye images. An intersection of the corneal bulge with the eye surface (e.g., sclera) can be determined as the limbic boundary. The determined limbic boundary can be used for iris segmentation or biometric applications. A head mounted display can include a camera that images the eye and a processor that analyzes the eye images and determines the limbic boundary.

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Nizhny Novgorod, Russia

Alexey Spizhevoy