Abstract: A method for determining a series of gaze positions of at least one eye over time is provided. The method comprises capturing a video of a user's face simultaneously with displaying a stimulus video on a screen and extracting at least one color component for each one of a plurality of images obtained from the video of the user's face. Based on the at least one color component for each one of the plurality of images, the series of gaze positions of the user's face over the time of the video is determined. A system for determining a series of gaze positions of at least one eye over time is also provided.

Abstract: The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Abstract: The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Abstract: A method for determining a series of gaze positions of at least one eye over time is provided. The method comprises capturing a video of a user's face simultaneously with displaying a stimulus video on a screen and extracting at least one color component for each one of a plurality of images obtained from the video of the user's face. Based on the at least one color component for each one of the plurality of images, the series of gaze positions of the user's face over the time of the video is determined. A system for determining a series of gaze positions of at least one eye over time is also provided.

Abstract: A method for training a neural network for determining a gaze position of at least one eye in an initial image comprising the at least one eye. A plurality of training initial images are obtained, of which at least one training color component image is extracted, each of the training initial images respectively comprising at least one eye and a known gaze position. Those are fed into a neural network outputting a respective internal representation for each one of the at least one component image. The neural network is trained by readjusting weights in the neural network to have the respective internal representation for each one of the at least one training color component image more consistent with a respective one of the known gaze position. Once trained, the neural network is used to determine the estimated gaze position relative to a screen of an electronic device.

Abstract: A method for training a neural network for determining a gaze position of at least one eye in an initial image comprising the at least one eye. A plurality of training initial images are obtained, of which at least one training color component image is extracted, each of the training initial images respectively comprising at least one eye and a known gaze position. Those are fed into a neural network outputting a respective internal representation for each one of the at least one component image. The neural network is trained by readjusting weights in the neural network to have the respective internal representation for each one of the at least one training color component image more consistent with a respective one of the known gaze position. Once trained, the neural network is used to determine the estimated gaze position relative to a screen of an electronic device.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.