Abstract: A method for determining a gaze convergence distance for a user of an eye tracking system. The method involves identifying a plurality of candidate convergence points along a combined gaze vector; and for each candidate convergence point, calculating a probability that the candidate convergence point is at the gaze convergence distance based on: a) the angle between a left gaze vector and a line from a left eye origin through the candidate convergence point; b) the angle between a right gaze vector and a line from a right eye origin through the candidate convergence point; c) the distance between the left gaze vector and the candidate convergence point; and d) the distance between the right gaze vector and the candidate convergence point. The method then involves calculating the gaze convergence distance based on the candidate convergence point that has the highest probability.

Abstract: A method for determining a gaze convergence function for a user of an eye tracking system. The method comprising presenting a first calibration object to the user on one or more display screens, wherein the first calibration object appears to be at a known first object distance from the user. While the first calibration object is being presented, the method: captures a calibration images of the user's eyes and calculates a first interpupillary distance, IPD, value. The method performs similar processing steps for a second calibration object to calculate a second IPD value. The method then determines a gaze convergence function which defines a relationship between IPD values and gaze convergence distances based on: i) the first IPD value and the associated first object distance; and ii) the second IPD value and the associated second object distance.

Abstract: An illuminator system for an eye-tracking system for tracking movements of an eye, the system comprising a plurality of illuminators; wherein each of the plurality of illuminators is configured to emit light forming a respective predetermined reference pattern; wherein each of the reference patterns, when reflected off the cornea of the eye, produces a respective reflected pattern forming a single glint; wherein the reference patterns of at least two of the plurality of the illuminators are different from each other.

Abstract: A system, a head-mounted device, a computer program, a carrier, and a method for a head-mounted device comprising an eye tracking sensor, for updating an eye tracking model in relation to an eye are disclosed. First sensor data in relation to the eye are obtained by means of the eye tracking sensor. After obtaining the first sensor data, the eye tracking sensor is moved in relation to the eye. After moving the eye tracking sensor, second sensor data in relation to the eye are obtained by means of the eye tracking sensor. The eye tracking model in relation to the eye is then updated based on the first sensor data and the second sensor data.

Abstract: An eye tracking system comprising: a plurality of light sources that are arranged to illuminate a user's eye when the eye tracking system is in use; and a controller configured to: receive a first-image of a surface, acquired while the surface is illuminated by a first set of the plurality of light sources; receive a second-image of the surface, acquired while the surface is illuminated by a second set of the plurality of light sources, wherein the second set of light sources is different to the first set of light sources; process the first-image and the second-image to determine an illumination contribution of one or more of the light sources; and determine light-source-control-signaling for one or more of the light sources based on the determined illumination contribution of the one or more of the light sources.

Abstract: An eye tracking system configured to: receive a plurality of right eye images of a right eye of a user; receive a plurality of left eye images of a left eye of a user, each left eye image corresponding to a right eye image in the plurality of right eye images; detect a pupil and determine an associated pupil signal, for each of the plurality of right eye images and each of the plurality of left eye images; calculate a right eye pupil variation of the pupil signals for the plurality of right eye images and a left eye pupil variation of the pupil signals for the plurality of left eye images; and determine a right eye weighting and a left eye weighting based on the right eye pupil variation and the left eye pupil variation.

Abstract: Images of an eye are captured by a camera. For each of the images, gaze data is obtained and a position of a pupil center is estimated in the image. The gaze data indicates a gaze point and/or gaze direction of the eye when the image was captured. A mapping is calibrated using the obtained gaze data and the estimated positions of the pupil center. The mapping maps positions of the pupil center in images captured by the camera to gaze points at a surface, or to gaze directions. A further image of the eye is captured by the camera. A position of the pupil center is estimated in the further image. Gaze tracking is performed using the calibrated mapping and the estimated position of the pupil center in the further image. These steps may for example be performed at a HMD.

Abstract: 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.

Abstract: An eye tracking system configured to: receive a plurality of right-eye-images of a right eye of a user; receive a plurality of left-eye-images of a left eye of a user, each left-eye-image corresponding to a right-eye-image in the plurality of right-eye-images; detect a pupil and determine an associated pupil-signal, for each of the plurality of right-eye-images and each of the plurality of left-eye-images; calculate a right-eye-pupil-variation of the pupil-signals for the plurality of right-eye-images and a left-eye-pupil-variation of the pupil-signals for the plurality of left-eye-images; and determine a right-eye-weighting and a left-eye-weighting based on the right-eye-pupil-variation and the left-eye-pupil-variation.

Abstract: Disclosed is a method for detecting a shadow in an image of an eye region of a user wearing a Head Mounted Device, HMD. The method comprises obtaining, from a camera of the HMD, an image of the eye region of the user wearing a HMD and determining an area of interest in the image, the area of interest comprising a plurality of subareas. The method further comprises determining a first brightness level for a first subarea of the plurality of subareas and determining a second brightness level for a second subarea of the plurality of subareas. The method further comprises comparing the first brightness level with the second brightness level, and, based on the comparing, selectively generating a signal indicating a shadow.

Abstract: 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.

Abstract: An eye tracking system configured to: receive a plurality of right-eye-images of a right eye of a user; receive a plurality of left-eye-images of a left eye of a user, each left-eye-image corresponding to a right-eye-image in the plurality of right-eye-images; detect a pupil and determine an associated pupil-signal, for each of the plurality of right-eye-images and each of the plurality of left-eye-images; calculate a right-eye-pupil-variation of the pupil-signals for the plurality of right-eye-images and a left-eye-pupil-variation of the pupil-signals for the plurality of left-eye-images; and determine a right-eye-weighting and a left-eye-weighting based on the right-eye-pupil-variation and the left-eye-pupil-variation.

Abstract: Images of an eye are captured by a camera. For each of the images, gaze data is obtained and a position of a pupil center is estimated in the image. The gaze data indicates a gaze point and/or gaze direction of the eye when the image was captured. A mapping is calibrated using the obtained gaze data and the estimated positions of the pupil center. The mapping maps positions of the pupil center in images captured by the camera to gaze points at a surface, or to gaze directions. A further image of the eye is captured by the camera. A position of the pupil center is estimated in the further image. Gaze tracking is performed using the calibrated mapping and the estimated position of the pupil center in the further image. These steps may for example be performed at a HMD.

Abstract: An eye tracking system is provided that detects the presence of problematic blobs in an image captured by the system and removes these problematic blobs by switching off illuminators. Problematic blobs may be those obscuring the pupil of the eye of the user. Each blob is detected in a first image by the use of at least one first criterion, and then an illuminator is switched off. After the illuminator is switched off, at least one second criterion is used to identify blobs in a subsequent image. This process may be repeated until the illuminator causing the problematic blob is identified.

Abstract: Images of an eye are captured at respective time instances by a camera of a head-mounted device. For each time instance, a position of a center of corneal curvature is estimated using an image captured at that time instance, a position of a pupil center is estimated using an image captured at that time instance, and a line is determined through the estimated corneal curvature center position and the estimated pupil center position. A first estimated position of a center of the eye is computed based on the lines determined for time instances in a first time period. A second estimated position of the center of the eye is computed based on the lines determined for time instances in a second time period. Relocation of the head-mounted device relative to a user's head is detected based on the first and second estimated positions of the eye center.

Abstract: Images of an eye are captured by a camera. For each of the images, gaze data is obtained and a position of a pupil center is estimated in the image. The gaze data indicates a gaze point and/or gaze direction of the eye when the image was captured. A mapping is calibrated using the obtained gaze data and the estimated positions of the pupil center. The mapping maps positions of the pupil center in images captured by the camera to gaze points at a surface, or to gaze directions. A further image of the eye is captured by the camera. A position of the pupil center is estimated in the further image. Gaze tracking is performed using the calibrated mapping and the estimated position of the pupil center in the further image. These steps may for example be performed at a HMD.

Abstract: A method of identifying scleral reflections in an eye tracking system is disclosed. A glint is identified in an image from an image sensor, wherein the glint is a representation in the image of a reflection of light from a cornea of the eye of the user or from a sclera of the eye of the user. A first pixel intensity of the glint is determined, a second pixel intensity of neighbor pixels of the glint is determined, and an absolute value of the difference between the first pixel intensity of the glint and the second pixel intensity of the neighbor pixels of the glint is determined. The glint is identified as a representation of a reflection from the sclera of the eye of the user on condition that the determined absolute value of the difference is below a predetermine threshold value.

Abstract: The invention is related to a method for calibrating an eye tracking device within a head-mounted display (HMD) comprising the steps of acquiring with the HMD via an image sensor, at least one optical target image from an optical target, wherein the optical target contains image points in a pattern, indexing image points within the optical target image wherein the image points are indexed by, selecting a rigid region of the optical target image, assigning indices to image points within the rigid region, fitting a polynomial approximation function to at least one column and one row of the image points of the region, predicting the location of at least one image point using the fitted polynomial approximation function, assigning the predicted image point an index, inputting indexed image points into an optimization algorithm that calculates a hardware calibration of the HMD, and writing hardware calibration values calculated from the optimization algorithm to the HMD unit.

Abstract: A system, a head-mounted device, a computer program, a carrier, and a method for a head-mounted device comprising an eye tracking sensor, for updating an eye tracking model in relation to an eye are disclosed. First sensor data in relation to the eye are obtained by means of the eye tracking sensor. After obtaining the first sensor data, the eye tracking sensor is moved in relation to the eye. After moving the eye tracking sensor, second sensor data in relation to the eye are obtained by means of the eye tracking sensor. The eye tracking model in relation to the eye is then updated based on the first sensor data and the second sensor data.

Abstract: Images of an eye are captured at respective time instances by a camera of a head-mounted device. For each time instance, a position of a center of corneal curvature is estimated using an image captured at that time instance, a position of a pupil center is estimated using an image captured at that time instance, and a line is determined through the estimated corneal curvature center position and the estimated pupil center position. A first estimated position of a center of the eye is computed based on the lines determined for time instances in a first time period. A second estimated position of the center of the eye is computed based on the lines determined for time instances in a second time period. Relocation of the head-mounted device relative to a user's head is detected based on the first and second estimated positions of the eye center.