Abstract: In an implementation, one or more user interactions from a virtual reality (VR) device user for initiating a service in a VR scenario of a VR application are detected. One or more eye physiological characteristics of the VR device user are obtained for user identity authentication. The obtained one or more eye physiological characteristics are compared with one or more pre-stored eye physiological characteristics samples. The VR device user is authenticated if the one or more eye physiological characteristics match at least a portion of the one or more pre-stored eye physiological characteristics samples, and a server that provides and performs the service is communicated with.

Abstract: The present invention relates to an eye movement measurement method including the steps of: (a) presenting standardized nystagmus test items and receiving am image of an eye in accordance with the standardized nystagmus test items; (b) recognizing a pupil and an iris from the image of the eye; (c) calculating amounts of horizontal and vertical changes of the pupil and an amount of torsional movement of the iris; (d) determining change values and orientations for three axis directions of the eye on the basis of the amounts of the horizontal and vertical changes of the pupil and the amount of the torsional movement of the iris; and (e) generating a diagnosis result for vertigo through deep learning modeling on the basis of the change values and orientations of the three axis directions of the eye.

Abstract: 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.

Abstract: Systems and methods for detecting textured contact lenses in an iris image are disclosed. Initially, “K” images are taken of an eye in near-infrared light, each for a different positioning of the illuminant. Three-dimensional properties of the hypothetical iris are estimated based on image processing methods. The variability of the estimated three-dimensional properties is calculated, and a classification into two classes is made based on the variability. A low variability denotes that an authentic iris pattern was presented to the sensor, whereas a high variability denotes that the sensor observes an eye wearing a textured contact lens. The systems and methods disclosed allow for detecting presentation attacks to iris recognition systems/sensors based on presentation of an eye wearing a textured contact lens in an automatic and accurate way.

Abstract: A device for providing even illumination to a user’s hand or other object to acquire images of the object for biometric identification of a user, includes a camera, a first light module positioned on a first side of the camera, and a second light module positioned on a second side of the camera. The camera is positioned closer to the second light module. The second light module emits light with a lesser intensity than the first light module. Light bending films positioned adjacent to the light modules redirect emitted light toward opposite edges of the field of view of the camera. Diffusers in the path of the emitted light diffuse portions of the emitted light along the edges of the field of view. Polarizers positioned in the path of the emitted light polarize the emitted light to facilitate visualization of particular features of the hand or other object.

Abstract: The present disclosure relates to a method and a system for imaging a biological tissue. A monochromatic image of the biological tissue is obtained. A texture analysis of the biological tissue is performed using spatial information of the monochromatic image to identity features of the biological tissue. A texture image is generated based on the features of the biological tissue. The biological tissue of the subject is classified as normal or abnormal at least in part based on a comparison between first order statistics of the texture image and predetermined values.

Abstract: The present disclosure provides a method for facilitating biometric recognition. Further, the method includes receiving, using a communication device, a biometric data from a user device. Further, the biometric data includes an eyeball image data. Further, the eyeball image data includes a periocular region image and an iris image. Further, the method includes processing, using a processing device, the biometric data using a machine learning model. Further, the method includes determining, using the processing device, an iris feature based on the processing. Further, the method includes determining, using the processing device, a periocular feature based on the processing. Further, the method includes concatenating, using the processing device, the iris feature and the periocular feature. Further, the method includes generating, using the processing device, an enrolled image data based on the concatenating. Further, the method includes storing, using a storage device, the enrolled image data in a database.

Abstract: Disclosed herein are methods and systems for determining liveness of a user, comprising analyzing visual content of a screen of a client device used by a user to access an online service, adjusting one or more visual objects displayed on the screen of the client device according to dynamically changing patterns, capturing a sequence of images depicting one or more reflecting surfaces associated with the user viewing the screen while the visual objects are displayed, analyzing the images to identify a reflection of the displayed visual objects in the reflecting surfaces and verifying liveness of the user based on one or more of a plurality of reflection attributes of the identified reflection.

Abstract: The invention belongs to scene segmentation's field in computer vision and is a depth-aware method for mirror segmentation. PDNet successively includes a multi-layer feature extractor, a positioning module, and a delineating module. The multi-layer feature extractor uses a traditional feature extraction network to obtain contextual features; the positioning module combines RGB feature information with depth feature information to initially determine the position of the mirror in the image; the delineating module is based on the image RGB feature information, combined with depth information to adjust and determine the boundary of the mirror. This method is the first method that uses both RGB image and depth image to achieve mirror segmentation in an image. The present invention has also been further tested. For mirrors with a large area in a complex environment, the PDNet segmentation results are still excellent, and the results at the boundary of the mirrors are also satisfactory.

Abstract: An access control system can include a camera that detects an image regarding a subject, a database maintaining information, a facial recognizer that receives the image and identifies a facial feature of the subject based on the received image and the facial information maintained by the database, and a processor to alert security or another user when an authorized user accessing an area of a building is under duress, when an unauthorized user is accessing an area of a building by tailgating after an authorized user, when an authorized user has not provided two-factor authentication, or when an authorized user is in a particular area of the building.

Abstract: A processor-implemented method of performing authentication includes obtaining a first biometric information of a user according to a first modality; calculating a first score based on the first biometric information; filtering the first score; determining whether the filtered first score satisfies a second condition; and selectively, based on a result of the determining, authenticating the user based on the first score and a first condition corresponding to the first modality. The second condition is different from the first condition.

Abstract: Systems and methods for detecting a traumatic brain injury (TBI). The system comprises a sensing arrangement and a control unit. The sensing arrangement collects eye movement data of a user. The control unit is in communication with the sensing arrangement and configured to compare the eye movement data to one or more baseline measurements of eye movement dynamics. The control unit is also configured to generate an alert indicating the presence or severity of the TBI for delivery to control unit administrator if the eye movement data diverges from one or more of the baseline measurements by a threshold amount.

Abstract: In one implementation, a method includes: while presenting reference CGR content, obtaining a request from a user to invoke a target state for the user; generating, based on a user model and the reference CGR content, modified CGR content to invoke the target state for the user; presenting the modified CGR content; after presenting the modified CGR content, determining a resultant state of the user; in accordance with a determination that the resultant state of the user corresponds to the target state for the user, updating the user model to indicate that the modified CGR content successfully invoked the target state for the user; and in accordance with a determination that the resultant state of the user does not correspond to the target state for the user, updating the user model to indicate that the modified CGR content did not successfully invoke the target state for the user.

Abstract: A system and a method of calculating coordinates of a pupil center point are provided. The system for acquiring the coordinates of the pupil center point includes a first camera, a second camera, a storage and a processor. The first camera is configured to capture a first image including a face and output the first image to the processor, the second camera is configured to capture a second image including a pupil and output the second image to the processor, a resolution of the first camera is smaller than a resolution of the second camera, and the storage is configured to store processing data, and the processor is configured to: acquire the first image and the second image; extract a first eye region corresponding to an eye from the first image; convert the first eye region into the second image, to acquire a second eye region corresponding to the eye in the second image; and detect a pupil in the second eye region and acquire the coordinates of the pupil center point.

Abstract: A spoofing detection apparatus comprises obtaining, from an image capture apparatus, a first image frame that includes the face of a subject person obtained when a light-emitting apparatus is emitting light and a second image frame that includes the face of the subject person obtained when the light-emitting apparatus is turned off, extracting, from the first image frame, information specifying a face portion of the subject person, and extract, from the second image frame, information specifying a face portion of the subject person, extracting a portion that includes a bright point formed by reflection in an iris region of an eye of the subject person, from the first image frame, extracts a portion corresponding to the portion that includes the bright point, from the second image frame, and calculates a feature that is independent of the position of the bright point, and determining authenticity of subject person based on the feature.

Abstract: Methods, systems, and computer-readable storage media for determining that a subject is a live person include obtaining a first image captured using a first camera disposed on a first side of a mobile device and obtaining a second image captured using a second camera disposed on a second side of the mobile device that is on the opposite side of the first side. The first image includes a representation of reflections visible on the corneas of a subject. The first image and the second image are pre-processing to generate a third image and a fourth image, respectively, where a first field of view represented in the third image at least partially overlaps with a second field of view of the fourth image. A determination is made, based on the third and fourth images, that a scene represented in the first field of view is substantially same as a scene represented in the second field of view.

Abstract: An example embodiment includes: an extraction unit that extracts a determination object image including a side part of an outer circumference of an iris from an image including an eye; and a determination unit that determines whether or not a colored contact lens is worn based on the determination object image.

Abstract: Device, method, and system of high-speed eye tracking. A system includes: a set of illumination sources, to selectively generate illumination pulses towards a human eye in accordance with a particular timing scheme per illumination source and in accordance with a particular wavelength per illumination source; a set of optical sensors, to sense changes to one or more properties of reflections of the illumination pulses from the human eye in response to modifications in operational properties of the illumination sources; and a processor to process the sensed changes, and to generate one or more processing results that are based on the sensed changes.

Abstract: An electronic device is disclosed comprising: a frame configured to be worn on a head of a user; a first light emitter mounted on the frame and configured to illuminate a first eye of the user, wherein an intensity and a spectrum of the first light emitter is adjustable; a second light emitter mounted on the frame and configured to illuminate a second eye of the user, wherein an intensity and a spectrum of the second light emitter is adjustable; a clock; a memory configured to store a target light profile comprising time-resolved data on an amount of light to be accumulated over time by the first and/or second eye of the user, wherein the time-resolved data comprises information about spectrum and intensity; and a controller configured to control the intensity and spectrum of the first and second light emitter based on the time and the target light profile.

Abstract: A system for dynamically masking items containing sensitive information on a display screen of a user device is disclosed. A distance of each viewer from the display screen is determined. Each viewer is identified using a facial recognition algorithm. Each viewer's authority level to view certain information is determined. For each item containing sensitive information, a dynamic proximity threshold for the item is determined based on a size of the display screen and a size of the item. The dynamic proximity threshold for the item is a distance from the display screen from which the item is identifiable. The system is configured to determine whether each viewer is authorized to view the item based on the authority level of each viewer. The item is masked if at least one viewer is at a distance that is within the dynamic proximity threshold and is not authorized to view the item.

Abstract: An artificial intelligence-based control method is disclosed. In an artificial intelligence-based control method according to an exemplary embodiment of the present disclosure, when a user approaches within a set sensing range of a device, the device may capture a user image and predict whether the user has an intent to use the device by using motion features included in the captured image. An AI control method of the present disclosure may be associated with an artificial intelligent module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a 5G service-related device, etc.

Abstract: An alignment system may comprise a housing defining a channel having a first end and a second end; a light source disposed at the first end of the channel; and an alignment image disposed at the second end of the channel. The light source may be in optical communication with the alignment image. The alignment system may further comprise a baffle extending around at least a portion of an inner perimeter of the channel, the baffle defining a backlight aperture. The light source may be in selective optical communication with the alignment image.

Abstract: Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

Abstract: An example method may include applying an automated face detection program implemented on a computing device to a plurality of training digital images associated with a particular media content program to identify a sub-plurality of the training digital images, each containing a single face of a particular person associated with the particular media content program. An automated feature extraction program may be applied to the sub-plurality to generate a set of feature vectors associated with the particular person, each feature vector of the set corresponding to a different training digital image. An automated face recognition program may be applied to a runtime digital image associated with the particular media content program to recognize the particular person, together with respective geometric coordinates. The runtime digital image may be stored together with information identifying the particular person and the respective geometric coordinates of the particular person in the runtime digital image.

Abstract: An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented face recognition program together with a set of computational models associated with the particular media content program may be applied to the digital image to recognize one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the set of the computational models may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

Abstract: System, devices and methods for monitoring pupillary response(s) are presented; the system comprises a light-blocking enclosure for receiving a first eye and a second eye of an individual, a light source for emitting visible light inside the enclosure and illuminating said first eye and/or second eye according to a predetermined illumination schedule, an image capturing unit for receiving visible light reflected from the first eye and/or the second eye while each eye being illuminated by said light source, and for capturing a plurality of images at a specific rate of the first eye and/or the second eye, a control unit for controlling operation of the light source and the image capturing unit and for receiving first data indicative of the plurality of images, a processing unit for processing the first data to determine second data indicative of size of pupil(s) of the first and/or second eye(s) in each of said plurality of images and processing the second data to enable determining an ophthalmological conditio

Abstract: Disclosed herein are methods and systems for determining liveness of a user, comprising analyzing visual content of a screen of a client device used by a user to access an online service, adjusting one or more visual objects displayed on the screen of the client device according to dynamically changing patterns, capturing a sequence of images depicting one or more reflecting surfaces associated with the user viewing the screen while the visual objects are displayed, analyzing the images to identify a reflection of the displayed visual objects in the reflecting surfaces and verifying liveness of the user based on one or more of a plurality of reflection attributes of the identified reflection.

Abstract: Systems, methods, and computer-readable media for facilitating an authentication processing service are provided.

Abstract: Systems and methods are presented for identifying individuals through facial recognition, voice recognition, or the like, recalling past recorded conversations with the identified individuals, and recording and inventorying conversations with the individuals with mobile devices. In some embodiments, a method is presented. The method may include identifying, at a device, an individual through facial recognition, voice recognition, or a unique RFID. The method may also include recording a conversation with the identified individual, and recalling past relevant recorded conversations based on the identification of the individual, and transmitting the recording of the conversation to a display system configured to display the recording of the event.

Abstract: An image acquisition system determines first and second sets of points defining an iris-pupil boundary and an iris-sclera boundary in an acquired image; determines respective ellipses fitting the first and second sets of points; determines a transformation to transform one of the ellipses into a circle on a corresponding plane; using the determined transformation, transforms the selected ellipse into a circle on the plane; using the determined transformation, transforms the other ellipse into a transformed ellipse on the plane; determines a plurality of ellipses on the plane for defining an iris grid, by interpolating a plurality of ellipses between the circle and the transformed ellipse; moves the determined grid ellipses onto the iris in the image using an inverse transformation of the determined transformation; and extracts an iris texture by unwrapping the iris and interpolating image pixel values at each grid point defined along each of the grid ellipses.

Abstract: A wearable heads-up display (WHUD) obtains attribute data corresponding to an attribute of an item of interest and obtains environmental data of an environment surrounding the WHUD via one or more sensors of the WHUD. The WHUD compares the attribute data with the environmental data to detect the item of interest. In response to the detection, the WHUD obtains location data indicative of a location of the item of interest, stores the location data in association with a context of detection of the item of interest. In response to a trigger, such as a query by a user regarding the item of interest, the WHUD provides a location indication based on the location data, the location indication including, for example, a display of a description of the location of the item of interest, a display of the item of interest at the location, and the like.

Abstract: 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.

Abstract: The present disclosure advantageously provides an Optical Hardware Accelerator (OHA) for an Artificial Neural Network (ANN) that includes a communication bus interface, a memory, a controller, and an optical computing engine (OCE). The OCE is configured to execute an ANN model with ANN weights. Each ANN weight includes a quantized phase shift value ?i and a phase shift value ?i. The OCE includes a digital-to-optical (D/O) converter configured to generate input optical signals based on the input data, an optical neural network (ONN) configured to generate output optical signals based on the input optical signals, and an optical-to-digital (O/D) converter configured to generate the output data based on the output optical signals. The ONN includes a plurality of optical units (OUs), and each OU includes an optical multiply and accumulate (OMAC) module.

Abstract: The disclosure relates to systems, methods and programs for anatomically-constrained gaze estimation. More specifically, the disclosure is directed to systems, methods and programs for providing a point of reference (PoR) in a 3D field and/or 2D plane, based on 6 DOF head pose constrained by the eyeball center being fixed in a common 3D coordinate system.

Abstract: A method for protecting a biometric template, comprising the steps of: retrieving an original vector (V) representing said biometric template, said vector comprising a plurality of original elements (v1, v2, . . . vi, . . . , vn); mapping at least some elements from said original vector to a protected vector (P) comprising a plurality of protected elements (p1, p2, . . . pi, . . . , pn?m+1), the mapping being based on multivariate polynomials defined by m user-specific coefficients (C) and exponents (E).

Abstract: A method of enrolling a user at a biometric lockset is described. The method includes receiving user access information from a mobile device of an administrative user of the biometric lockset. The user access information indicates to the biometric lockset to enter an enrollment mode in which a user identity is associated with fingerprint data in a user entry within a memory of the biometric lockset. A plurality of different light codes are displayed on the lockset, each one of the plurality of light codes representative of a different state of fingerprint data capture. A message is transmitted to the mobile device, and corresponds to the light code displayed at the lockset. The completed fingerprint data is stored in association with the user identity of the user in the user entry.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.

Abstract: A narrow angle illumination light source for an ophthalmic surgical laser system includes multiple light emitting diodes (LEDs), multiple corresponding ball lenses, multiple corresponding upper apertures located between the LEDs and the lenses (optional), and multiple corresponding lower apertures located below the lenses. The light passing through each upper aperture and corresponding lens forms a light cone having a defined divergence angle and cone axis angle; the light cone only illuminates the corneal and sclera of a docked eye without illuminating the patients nose and orbit. The lower apertures may have distinctive shapes to aid video focusing. The multiple LEDs are distributed uniformly in the circle, and may be divided into multiple independently controllable segments which allows directional illumination. The LEDs also have controllable brightness to allow images of darker and brighter illuminations to be taken in short succession.

Abstract: An apparatus includes an interface and a processor. The interface may be configured to receive video frames corresponding to an interior of a vehicle. The processor may be configured to perform video operations on the video frames to detect objects in the video frames, detect a driver based on the objects detected in the video frames, detect a use of an electronic device by the driver and generate a notification signal. The notification signal may be configured to warn the driver about using the electronic device in the vehicle. The notification signal may be generated if the use of the electronic device is unauthorized. The processor may comprise a dedicated hardware module configured to perform feature detection to calculate descriptors for determining a likelihood that pixels of the video frames belong to the objects that correspond to the use of the electronic device by the driver.

Abstract: A gaze tracking method includes the following steps. Firstly, a to-be-analyzed facial image is captured. Then, whether the to-be-analyzed facial image conforms to a customized 3D face model is determined If not, a customized 3D face model of the to-be-analyzed facial image is created. If yes, an eye area image of the to-be-analyzed facial image is obtained. Then, according to the customized 3D face model and the to-be-analyzed facial image, head posture information is obtained. Then, an eye camera coordinate value referenced to a camera coordinate system is obtained. Then, the eye camera coordinate value is converted into an eye frame coordinate value referenced to a display frame coordinate system. Then, according to the eye frame coordinate value, the head posture information and an eyeball radius, an eyeball center point coordinate value is obtained, and accordingly a gaze coordinate value is obtained.

Abstract: An eyeball detection unit of the present disclosure includes an irradiator that projects substantially parallel illumination light toward a cornea of an eyeball, a detector that detects light intensity of reflected light from the cornea, and a detection controller that identifies a centroid offset in an angular direction of the light intensity of the reflected light from the cornea with respect to a reference optical axis on the basis of a detected value by the detector, and thereafter calculates a positional offset of a cornea position with respect to the reference optical axis.

Abstract: A gaze-point determining method, a contrast adjusting method and a contrast adjusting apparatus for a virtual reality device, a virtual reality device and a storage medium. The contrast adjusting method includes: determining a coordinate of a gaze point in a display region viewed by a user; determining a gaze region of the user based on the coordinate of the gaze point; and adjusting brightness of the gaze region.

Abstract: One embodiment of the present disclosure sets forth a method that includes receiving one or more two-dimensional images of one or more light patterns incident on an eye proximate to an eye region of a near-eye display device, and computing a gaze direction associated with the eye based on the one or more two-dimensional images.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: extract a plurality of feature points from a biometric image; calculate, for each of the feature points, respective feature vectors including the respective feature points from the biometric image; calculate bit strings by binarizing a value of each of dimensions for each of the calculated feature vectors; attach reliability to each of the calculated bit strings based on stability of the binarizing of the corresponding feature vector; and select a specific number of the bit strings from the calculated bit strings in accordance with values of the reliability.

Abstract: Provided is an identification method for an identification system, which includes a sensing area and an image sensor. First, a test object is close to the sensing area, so that the image sensor generates a dynamic image. Next, the test object gradually pressurizes the sensing area. Then, the test object completely covers the sensing area, and the image sensor further produces a perspective image. Finally, an identification module is used to determine whether the dynamic image is a biological image according to the perspective image, and to perform a subtraction operation on the dynamic image as a basis to determine whether to unlock the identification system. Therefore, the identification system and the identification method can achieve a real-time determination on whether the dynamic image is a biological image. Also, the identification method greatly improves the false acceptance rate (FAR) and the false rejection rate (FRR) of the identification system.

Abstract: A face recognition device and a face recognition method are provided. The face recognition device includes a camera module, a processor and a memory. The camera module obtains a first image of a recognition target and obtains a second image of the recognition target. The processor analyzes whether the first image meets an image condition to determine whether the recognition target is a real face. The memory stores a face database. When the processor determines that the first image meets the image condition, the processor analyzes a plurality of facial features in the second image, and the processor compares whether the second image matches face registration data in the face database according to the facial features to determine whether the second image passes recognition.

Abstract: A biometrics-enabled portable storage device may store and secure data via biometrics related to a user's iris. The biometrics-enabled portable storage device may include a camera that captures image data related a user's iris and stores the image data to enroll the user for use of the biometrics-enabled portable storage device. To unlock the data, a user aligns the camera with their iris using a hot mirror and the camera captures iris data for comparison with the iris image data stored during enrollment. If the two sets of image data match, the biometrics-enabled portable storage device may be unlocked and the user may access data stored on the biometrics-enabled portable storage device. If the two sets of image data do not match, then the biometrics-enabled portable storage device remains locked.

Abstract: A delivery server is connected to a device which uses an image analysis engine to perform an image analysis on image data obtained by a monitoring camera. The delivery server includes an analysis engine storage unit which is configured to store a plurality of image analysis engines for each of which at least one of a shooting condition of the image data appropriate for the image analysis or a target object to be detected among objects which are possibly included in the image data is different; and a supplying unit which is configured to supply to the device an image analysis engine corresponding to at least one of the shooting condition of the image data obtained by the device or the target object which is set for the device, among the plurality of image analysis engines.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for authenticating a user based on passive affective and knowledge-based authentication (AKBA). In one aspect, a method includes data associated with eye movements and ocular dynamics of the user are captured with a camera as the user looks at a graphical user interface (GUI) of a device; an AKBA signature of the user is determined based on the captured data; the user is authenticated based on a comparison of the AKBA signature with an AKBA template associated with the user; and an access to a subset of functions of an application is granted.

Abstract: In various examples, a plurality of reference points may be generated by processing an image of an eye of a user from a vision sensor. The plurality of reference points may correspond to, for example, a boundary between a sclera of the eye and an eyelid of the user, a boundary between a pupil and a cornea, etc. Based on the reference points corresponding to the boundary between the sclera and the eyelid, a measure of the eye of the user may be detected.