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: at least 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; access an embedding (108, 608) in the non-transitory memory, the embedding comprising an embedding space representation that has a majority of elements that are statistically independent, wherein the embedding (108, 608) is learned using a deep neural network with a triplet network architecture, wherein the deep neural network is configured to learn the embedding (108, 608) from eye images of a plurality of persons, 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, process the first image of the eye using the embedding (108, 608) to generate an embedding space representation; process (716, 720) the embedding space representation using a 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. a display; an image capture device configured to capture a first image of an eye of a user; non-transitory memory configured to store: at least 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; access an embedding (108, 608) in the non-transitory memory, the embedding comprising an embedding space representation that has a majority of elements that are statistically independent, wherein the embedding (108, 608) is learned using a deep neural network with a triplet network architecture, wherein the deep neural network is configured to learn the embedding (108, 608) from eye images of a plurality of persons, 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, process the first image of the eye using the embedding (108, 608) to generate an embedding space representation; process (716, 720) the embedding space representation using a 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. receive the first image of the eye; access an embedding (108, 608) in the non-transitory memory, the embedding comprising an embedding space representation that has a majority of elements that are statistically independent, wherein the embedding (108, 608) is learned using a deep neural network with a triplet network architecture, wherein the deep neural network is configured to learn the embedding (108, 608) from eye images of a plurality of persons, 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, wherein the embedding (108, 608) is learned using a deep neural network with a triplet network architecture, wherein the deep neural network is configured to learn the embedding (108, 608) from eye images of a plurality of persons, 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, process the first image of the eye using the embedding (108, 608) to generate an embedding space representation; process (716, 720) the embedding space representation using a 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.