Disclosed is a head mounted electronic device including a body, a lens clip having at least one opening defined therein, wherein the lens clip is disposed on the body so as to face toward a face of a user, an infrared-ray emissive device disposed on one portion of the body, an infrared-ray imaging device disposed on an opposite portion of the body to one portion thereof, wherein the infrared-ray imaging device acquires a first eyeball image obtained when infrared-ray emitting from the infrared-ray emissive device is reflected from an eyeball of the user, and a processor operatively connected to the infrared-ray emissive device and the infrared-ray imaging device.

Techniques of tracking a user's gaze includes identifying a region of a display at which a gaze of a user is directed, the region including a plurality of pixels. By determining a region rather than a point, when the regions correspond to elements of a user interface, the improved technique enables a system to activate the element to which a determined region is selected. In some implementations, the system makes the determination using a classification engine including a convolutional neural network; such an engine takes as input images of the user's eye and outputs a list of probabilities that the gaze is directed to each of the regions.

An eye tracking system includes at least two cameras configured to register eye images of at least one eye. The system obtains eye images from at least one camera in a subset of the at least two cameras, determines a first pupil parameter based on a first eye image, and determines a second pupil parameter based on a second eye image. The system compares the first and second pupil parameters to obtain a test parameter and checks the test parameter against at least one operation criterion. Responsive to the checking, the system assigns a respective operation state to at least one camera in the subset. The operation state involves one of (A) operating the camera at a high frame rate, (B) operating the camera at a reduced frame rate being lower than high frame rate, (C) the camera being in a standby mode or (D) the camera being powered off.

An eye tracking system provides a quality measure of a calculated gaze of a user. The eye tracking system receives gaze data including left eye gaze data associated with a left eye of the user and right eye gaze data associated with a right eye of the user. The eye tracking system compares the left eye gaze data and the right eye gaze data to determine a gaze difference value. The eye tracking system provides a gaze quality value of the gaze data based on the gaze difference value.

Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.

The present invention relates to a system for alignment between real and virtual objects in a head-mounted optical see-through display. In an embodiment, the system includes a tracking system including a processor, a headgear attached with the head-mounted optical see-through display, the 5 head-mounted optical see-through display includes at least two cameras mounted on a rigid frame, at least one object may be fixed or mobile including a plurality of marker points, the tracking system is operatively coupled to the headgear and the object, the processor is configured for: capturing two perspective images of the substantially circular entrance pupil of at least one 0 eye and relaying the image data to the processor, a memory device coupled to the processor and containing the geometric calibration data of the at least two cameras and the pre-calibrated transformation between the cameras. The processor extracts the boundary between the entrance pupil and the iris, calculates the projected center of the boundary in the individual images and 5 using the calibration data estimates the center of the entrance pupil in three dimensional space in relation to the cameras.

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.

The present disclosure provides a method, device, computer apparatus, and storage medium for training recognition model and recognizing fundus features. The method includes: obtaining a color fundus image sample associated with a label value, inputting the color fundus image sample into a preset recognition model containing initial parameters; extracting a red channel image; inputting the red channel image into the first convolutional neural network to obtain a first recognition result and a feature image of the red channel image; combining the color fundus image sample with the feature image to generate a combined image, and inputting the combined image into the second convolutional neural network to obtain a second recognition result; obtaining a total loss value through a loss function, and when the total loss value is less than or equal to a preset loss threshold, ending the training of the preset recognition model.

The present invention provides an eye tracking system including: a pupil sensing unit sensing a pupil of a user by at least one sensor embedded in smart glasses; a display unit including smart lenses to which a plurality of target objects is floated; an eye tracking unit tracking an eye through at least one sensor and acquiring eye state information of the user for the plurality of target objects; and an input unit performing an input after selecting a specific object in which the eye of the user stays for a predetermined time or more among the plurality of target objects as an input object based on the eye state information, in which at least one of a size, a color, and a movement direction of the input object is changed based on the eye state information of the user.

An information processing device includes an acquisition unit, an identification unit and an emotion estimation unit. The acquisition unit acquires input information as information regarding a user in a certain situation. The identification unit identifies an object to which the user is paying attention and the user's appearance when the user is paying attention to the object based on the input information. The emotion estimation unit estimates emotion of the user based on object information indicating the identified object, appearance information indicating the identified appearance, and a predetermined method of estimating the emotion.