Technologies are described herein for an eye tracking that may be employed by devices and systems such as head mount display (HMD) devices. Light that is reflected from a user's eye may be specular or scattered. The specular light has an intensity or magnitude that may saturate the electronics. The presently disclosed techniques mitigate saturation by generating detected signals from an optical detector, evaluating the signal levels for the detected signal, and selectively gating the detected signals that have saturated. The remaining scattered signals can be combined to achieve a combined signal that can be converted into a digital signal without saturating the electronics, which can then be processed to form an image of the eye for identification purposes, for tracking eye movement, and for other uses. The described technologies provide a clear image without ambient light reflections or specular light interfering with the image.

An iris authentication device includes: a camera that is installed at a position lower than a line of sight of an authentication target person; lighting that is installed either at a same height as the position of the camera or at a position lower than the camera and that irradiates the authentication target person with light; and an authentication unit that performs authentication of the authentication target person by collating an iris that appears in an image of the authentication target person with a pre-registered iris image, the image of the authentication target person being captured by the camera from lower front while the lighting is irradiating the light.

The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: acquiring an iris image group comprising at least two iris images to be compared; detecting iris locations in the iris images and segmentation results of iris areas in the iris images; performing multi-scale feature extraction and multi-scale feature fusion on an image area corresponding to the iris locations, to obtain iris feature maps corresponding to the iris images; performing comparison using the segmentation results and the iris feature maps respectively corresponding to the at least two iris images, and determining whether the at least two iris images correspond to the same object based on a comparison result of the comparison. Embodiments of the present disclosure realize accurate comparison of iris images.

Various embodiments provide systems and methods for identifying impairment using measurement devices.

Methods and systems are provided for an AdHoc enrollment process. A user may be able to enroll and be verified by a system for a variety of actions or authentications without being forced to turn over personally identifiable information and without having to formally enroll. The system may compare captured biometric information with existing biometric information and may identify the user without the use of personally identifiable information.

Using various embodiments, systems and devices to access secure data using a wearable lens device are described. In one embodiment, the wearable lens device comprises at least one optical lens, a processing system, and, a display system, coupled to the processing system. The display system can be configured to present at least one of an augmented reality, virtual reality, and/or mixed reality artifact on the at least one optical lens. The augmented reality, virtual reality artifact, and/or mixed reality artifact can be related to secure data whose access is intended to be controlled and/or limited.

This application discloses an image feature recognition method performed at a computing device. The computing device obtains a first neural network model by training parameters in a second neural network model using a first training set and a second training set consecutively, image features of training pictures in the first training set being marked and image features of training pictures in the second training set being not marked. After obtaining a first neural network model, the computing device obtains a recognition request for recognizing an image feature in a target picture. The computing device then recognizes the image feature in the target picture by applying the target picture to the first neural network model. Finally the computing device returns a first recognition result of the first neural network model, the first recognition result indicating an image feature (for example, a pathological feature) recognized in the target picture.

An automated identity verification computing device, system and method receives an encrypted renewal request from a user computing device including a biometric value, a digital passport object identifier, and request data. The computing device, system and method retrieves trusted biometric data based on the digital passport object identifier from an identity database, determine a verification score based on the trusted biometric data and the biometric value, and generates a renewal package including the verification score, the digital passport object identifier, and the request data. The computing device, system and method transmit the renewal package to a sponsoring authority computing device.

Biometric gallery management is performed by association one or more wireless identifiers that correspond to one or more mobile devices (such as smart phones, tablet computing devices, cellular telephones, wearable devices, smart watches, fitness monitors, digital media players, medical devices, and/or other mobile computing devices) that people carry with digital representations of biometrics corresponding to the people. Wireless identifiers corresponding to mobile devices proximate to a biometric reader device may be monitored. Upon detection of wireless identifiers corresponding to mobile devices proximate to the biometric reader device, the associated digital representations of biometrics may be loaded from a main gallery into one or more local galleries, which may then be used to perform one or more biometric identifications and/or verifications.

A method for biometric identification or authentication is described. An image of a body region is obtained. A truth map for said body region is obtained, said truth map associating, with each portion of a set of portions of said image of a body region, a probability that said portion belongs to a true body region. The image of the body region is then compared with a group of reference biometric data using the truth map. Finally, the identification or authentication of said body region is validated or invalidated in response to said comparison.