The disclosure provides a system that may receive an identification of a first patient; may retrieve, based at least on the identification of the first patient, first eye identification information that includes a first plurality of iris structures associated with a first eye of the first patient; may determine a second plurality of iris structures of an eye of a current patient; may determine if the second plurality of iris structures match the first plurality of iris structures; if the second plurality of iris structures match the first plurality of iris structures, may provide an indication that the first eye has been correctly identified; and if the second plurality of iris structures do match the first plurality of iris structures, may provide an indication that the first eye has not been correctly identified.

To provide a display apparatus that makes it possible to further improve a performance in controlling video presentation according to characteristics of an eyeball of a user. A display apparatus is provided that includes a light source; a processor that performs processing on a distribution of characteristics of an eyeball; a monitoring section that monitors a state of the eyeball; a matching section that performs matching on the distribution of the characteristics of the eyeball and the state of the eyeball; and an irradiator that irradiates a specified position on a retina with video display light emitted by the light source.

There is provided an information acquisition apparatus including an output means for outputting guidance information for guiding a subject to move a head while gazing at a predetermined position, and an image acquisition means for acquiring an image including an iris of the subject after outputting the guidance information.

A system to perform remote oculography includes light-occluding goggles configured to be worn by a patient. The light-occluding goggles include an infrared camera positioned to capture one or more first images of a first eye of the patient. The light-occluding goggles also include a display positioned such that it is viewable by a second eye of the patient. The display is configured to display a pattern for the patient to view. The light-occluding goggles also include a sensor configured to detect information regarding a position of a head of the patient. The system also includes a visible light camera configured to capture one or more second images of the patient as the patient wears the light-occluding goggles.

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.

Systems and methods enabling rendering an avatar attuned to a user. The systems and methods include receiving audio-visual data of user communications of a user. Using the audio-visual data, the systems and methods may determine vocal characteristics of the user, facial action units representative of facial features of the user, and speech of the user based on a speech recognition model and/or natural language understanding model. Based on the vocal characteristics, an acoustic emotion metric can be determined. Based on the speech recognition data, a speech emotion metric may be determined. Based on the facial action units, a facial emotion metric may be determined. An emotional complex signature may be determined to represent an emotional state of the user for rendering the avatar attuned to the emotional state based on a combination of the acoustic emotion metric, the speech emotion metric and the facial emotion metric.

Method for determining a current gaze direction of a user in relation to a three-dimensional (“3D”) scene, the 3D scene being sampled by a rendering function to produce at least one projection image of the 3D scene, wherein the method comprises the steps: determining, by a reprojection means, a reprojection transformation to be applied to the projection image before being displayed so as to be visible to the user at a gaze time point; determining, by a gaze direction detection means, a physical gaze direction of the user at said gaze time point; and determining a modified gaze direction of the user at said gaze time point, the modified gaze direction being determined in relation to the 3D scene based on both the physical gaze direction and the reprojection transformation. The disclosure also relates to a system and to a computer software function.

There is provided a method of re-classifying a clinically significant feature of a medical image as an artifact, comprising: feeding a target medical image captured by a specific medical imaging sensor at a specific setup into a machine learning model, obtaining a target feature map as an outcome of the machine learning model, wherein the target feature map includes target features classified as clinically significant, analyzing the target feature map with respect to sample feature map(s) obtained as an outcome of the machine learning model fed a sample medical image captured by at least one of: the same specific medical imaging sensor and the same specific setup, wherein the sample feature map(s) includes sample features classified as clinically significant, identifying target feature(s) depicted in the target feature map having attributes matching sample feature(s) depicted in the sample feature map(s), and re-classifying the identified target feature(s) as an artifact.

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.

A method for a multi-factor authentication, the method receives results of an initial authentication of a user. Responsive to confirming the initial authentication, an image of a secondary set of authentication options is presented. An option selection is received from the user, wherein the selection is determined by tracking eye movement of the user over the image that includes the set of second factor authentication options. User facial activity is tracked corresponding to the selection made from the secondary set of authentication options. The monitored facial activity is compared to a pre-established authentication condition to determine whether a match exists with the selected secondary set of authentication options, and responsive to facial activity monitored matching the authentication condition pre-established by the user and corresponding to the selection made from the secondary set of authentication options, authentication of the user is confirmed.