DEEP NEURAL NETWORK FOR IRIS IDENTIFICATION
First Claim
1. A wearable display system comprising:
- a display;
an image capture device configured to capture a first image of an eye of a user;
non-transitory memory configured to store;
an embedding for processing the first image of the eye,wherein the embedding is learned using a deep neural network with a triplet network architecture,wherein the deep neural network is configured to learn the embedding from eye images of a plurality of persons, andwherein a distance in the embedding space representation for eye images from the same person is smaller than a distance in the embedding space representation for eye images from different persons,a classifier for processing the processed first image of the eye, and executable instructions; and
a hardware processor in communication with the display, the image capture device, and the non-transitory memory, the hardware processor programmed by the executable instructions to;
receive the first image of the eye;
process the first image of the eye using the embedding to generate an embedding space representation;
process the embedding space representation using the classifier to calculate a likelihood score that the first image of the eye is an image of an eye of an authorized user; and
grant or deny the user access to the wearable display system based on the likelihood score.
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Accused Products
Abstract
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.
105 Citations
20 Claims
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1. A wearable display system comprising:
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a display; an image capture device configured to capture a first image of an eye of a user; non-transitory memory configured to store; an embedding for processing the first image of the eye, wherein the embedding is learned using a deep neural network with a triplet network architecture, wherein the deep neural network is configured to learn the embedding from eye images of a plurality of persons, and wherein a distance in the embedding space representation for eye images from the same person is smaller than a distance in the embedding space representation for eye images from different persons, a classifier for processing the processed first image of the eye, and executable instructions; and a hardware processor in communication with the display, the image capture device, and the non-transitory memory, the hardware processor programmed by the executable instructions to; receive the first image of the eye; process the first image of the eye using the embedding to generate an embedding space representation; process the embedding space representation using the classifier to calculate a likelihood score that the first image of the eye is an image of an eye of an authorized user; and grant or deny the user access to the wearable display system based on the likelihood score. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for training a deep neural network for iris authentication, comprising:
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computer-readable memory storing executable instructions; and one or more hardware-based hardware processors programmed by the executable instructions to at least; access a deep neural network comprising a plurality of layers, wherein each layer of the plurality of layers is connected to at least another layer of the plurality of layers; provide the deep neural network with a training set comprising eye images of a plurality of persons; compute embedding space representations of the plurality of eye images using the deep neural network, wherein the embedding space representations of the plurality of eye images of the same person are within a threshold; and update the deep neural network based on the distances between the embedding space representations of eye images of the same persons and different persons. - View Dependent Claims (9, 10, 11, 12)
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13. A head mounted display system comprising:
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a display; an image capture device configured to capture a first image of an eye of a user; non-transitory memory configured to store executable instructions; and a hardware processor in communication with the display, the image capture device, and the non-transitory memory, the hardware processor programmed by the executable instructions to; receive the first image of the eye; process the first image of the eye to generate a representation of the first image of the eye in polar coordinates; process the representation of the first image of the eye in polar coordinates using a deep neural network to generate an embedding space representation; and process the embedding space representation using a classifier to generate a likelihood score that the image of the eye is an image of the authorized user'"'"'s eye. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification