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 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 systems and methods for extracting high resolution oculometric parameters and eye movement 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). The oculometric parameters are generated at a first temporal resolution. The oculometric parameters are up sampled to increase the temporal resolution to a second temporal resolution. The oculometric parameters are then processed to generate various eye movement parameters such as blink parameter, pupil response parameter, saccade parameter, anti-saccade parameter, fixation parameter, or smooth pursuit parameter. The oculometric parameters are synchronized with a video stimulus presented on a user device prior to generating the eye movement parameters.

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.