An autonomous light system may comprise a reading light assembly, an object detection device; and a controller. The reading light assembly may include a light source and an actuation system. The controller may be operably coupled to the light source and the object detection device. The controller may include an object detection module and may be configured to receive object data from the object detection device, compare the object data against an object database, identify an object based on the comparison of the object data to the object database, send a first command to at least one of the light source and the actuation system.

An image gaze correction method, apparatus, electronic device, computer-readable storage medium, and computer program product related to the field of artificial intelligence technologies are provided. The image gaze correction method includes: acquiring an eye image from an image; performing feature extraction processing on the eye image to obtain feature information of the eye image; performing, based on the feature information and a target gaze direction, gaze correction processing on the eye image to obtain an initially corrected eye image and an eye contour mask; performing, by using the eye contour mask, adjustment processing on the initially corrected eye image to obtain a corrected eye image; and generating a gaze corrected image based on the corrected eye image.

An information processing apparatus according to an embodiment of the present technology includes a line-of-sight estimator, a correction amount calculator, and a registration determination section. The line-of-sight estimator calculates an estimation vector obtained by estimating a direction of a line of sight of a user. The correction amount calculator calculates a correction amount related to the estimation vector on the basis of at least one object that is within a specified angular range that is set using the estimation vector as a reference. The registration determination section determines whether to register, in a data store, calibration data in which the estimation vector and the correction amount are associated with each other, on the basis of a parameter related to the at least one object within the specified angular range.

The present disclosure is directed to systems and methods for measuring and analyzing pupillary psychosensory responses. An electronic device is configured to receive video data with at least two frames. The electronic device then locates one or more eye objects in the video data and determine pupil and iris sizes of the one or more eye objects. The electronic device determines the pupillary psychosensory responses of the one or more eye objects by tracking a ratio of pupil diameter to iris diameter throughout the video. Several metrics for the pupillary psychosensory responses can be determined (e.g., velocity of change of the ratio, peak to peak amplitude of the change in ratio over time, etc.). These metrics can be used as measures of an individual's cognitive ability and mental health in a single session or tracked throughout multiple sessions.

A display system for a vehicle includes a display unit mounted to the vehicle and is selectively operable in a first mode as a holographic display and in a second mode as a mirror. Holographic images may include rear view images obtained from a camera or computer generated graphics. Holographic images are displayed at a virtual image plane behind the display to reduce the operator's eyes accommodation.

Embodiments of the present invention provide a display panel, including a display region, wherein the display region includes an iris recognition region; the display panel further includes a light conversion layer disposed in the iris recognition region, the light conversion layer is configured to convert visible light incident on the light conversion layer into infrared light, and the infrared light is emitted from a display side of the display panel.

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.

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.

A pupil detection device includes an eye area image obtaining unit that obtains image data representing an eye area image in a captured image obtained by a camera; a luminance gradient calculating unit that calculates luminance gradient vectors corresponding to respective individual image units in the eye area image, using the image data; an evaluation value calculating unit that calculates evaluation values corresponding to the respective individual image units, using the luminance gradient vectors; and a pupil location detecting unit that detects a pupil location in the eye area image, using the evaluation values.

Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.