Abstract: Disclosed are systems and methods for extracting high resolution oculometric parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius) at a first resolution. A deconvolution process is performed on the video stream to improve accuracy or resolution of the oculometric parameters, based on stimulus information of a video stimulus displayed on a client device associated with the user, environment data of an environment in which the user is located, device data of the client device, etc. The oculometric parameters are then processed using a prediction model that is trained based on high resolution oculometric parameters obtained using eye tracking devices to predict oculometric parameters at a resolution greater than the first resolution.

Abstract: Disclosed are system, method, and computer program product embodiments for determining a digital marker indicative of a neurological condition. An embodiment operates by detecting a region of interest in an image frame of a face of a user. The region of interest includes an eye of the user and the image frame is obtained from a video stream. The embodiment then generates a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, performs deconvolution on the portion of the image frame specified by the region of interest using the PSF, determines a set of oculometric parameters of the eye of the user using the deconvolved image, and determines one or more digital markers of the user based on the set of oculometric parameters. The one or more digital markers are indicative of a neurological condition of the user.

Abstract: Disclosed are system, method, and computer program product embodiments for determining a digital marker. An embodiment operates by determining one or more oculometric parameters associated with an eye of a user using a video stream. The video stream comprises at least one image of a face of the user. The embodiment then determines a pupillary response function of brightness or a gaze response function by performing a deconvolution process based on at least the one or more oculometric parameters. The pupillary response function of brightness or the gaze response function is indicative of a response of the eye of the user to an external factor. The embodiment determines one or more digital markers of the user based on the pupillary response function of brightness or the gaze response function. The one or more digital markers are indicative of a neurological condition or a mental health condition of the user.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for tracking a point of gaze of a subject in a manner that incorporates automatic eye tracking calibration. In an embodiment, video is obtained of the subject viewing content presented by a display. The video is analyzed to determine a series of time points at which a gaze of the subject is dwelling on a visual target and, for each time point in the series, a set of eye feature values. A location of a stimulus presented by the display is also obtained for each time point in the series. This information is utilized to determine a mapping that maps a set of eye feature values to a point of gaze. The mapping is used to map a set of eye features obtained by analyzing the video to the point of gaze of the subject.

Abstract: Disclosed herein are system, method, and computer program product embodiments, and/or combinations and sub-combinations thereof, for determining a time delay for synchronizing one or more parameters. In an embodiment, a plurality of frames is presented on a display device. A brightness level is modulated in one or more frames of the plurality of frames. A plurality of image frames of a face of a user is received. The plurality of image frames are obtained from a video stream recorded by an image sensor during the presentation of the plurality of frames on the display device. The plurality of image frames are processed to determine the time delay between when the modulating the brightness level is performed in the one or more frames and when the modulating the brightness level in the one or more frames is reported by the image sensor.

Abstract: Disclosed are systems and methods for extracting high resolution oculometric parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius) at a first resolution. A deconvolution process is performed on the video stream to improve accuracy or resolution of the oculometric parameters, based on stimulus information of a video stimulus displayed on a client device associated with the user, environment data of an environment in which the user is located, device data of the client device, etc. The oculometric parameters are then processed using a prediction model that is trained based on high resolution oculometric parameters obtained using eye tracking devices to predict oculometric parameters at a resolution greater than the first resolution.

Abstract: Disclosed are system, method, and computer program product embodiments for determining a digital marker indicative of a neurological condition. An embodiment operates by detecting a region of interest in an image frame of a face of a user. The region of interest includes an eye of the user and the image frame is obtained from a video stream. The embodiment then generates a point spread function (PSF) based on acquired data and a portion of the image frame specified by the region of interest, performs deconvolution on the portion of the image frame specified by the region of interest using the PSF, determines a set of oculometric parameters of the eye of the user using the deconvolved image, and determines one or more digital markers of the user based on the set of oculometric parameters. The one or more digital markers are indicative of a neurological condition of the user.