Abstract: A method includes determining a measured eye gaze position of an eye of a user. The method also includes determining a first incremental change in the measured eye gaze position by processing the measured eye gaze position by a long short-term memory (LSTM) model, and determining a first predicted eye gaze position of the eye at a first future time based on the measured eye gaze position and the first incremental change. The method additionally includes determining a second incremental change in the first predicted eye gaze position by processing the first predicted eye gaze position by the LSTM model, and determining a second predicted eye gaze position of the eye at a second future time subsequent to the first future time based on the first predicted eye gaze position and the second incremental change.

Abstract: A method includes determining a measured eye gaze position of an eye of a user. The method also includes determining a first incremental change in the measured eye gaze position by processing the measured eye gaze position by a long short-term memory (LSTM) model, and determining a first predicted eye gaze position of the eye at a first future time based on the measured eye gaze position and the first incremental change. The method additionally includes determining a second incremental change in the first predicted eye gaze position by processing the first predicted eye gaze position by the LSTM model, and determining a second predicted eye gaze position of the eye at a second future time subsequent to the first future time based on the first predicted eye gaze position and the second incremental change.

Abstract: The technology relates to methods and systems for implicit calibration for gaze tracking. This can include receiving, by a neural network module, display content that is associated with presentation on a display screen (1202). The neural network module may also receive uncalibrated gaze information, in which the uncalibrated gaze information includes an uncalibrated gaze trajectory that is associated with a viewer gaze of the display content on the display screen (1204). A selected function is applied by the neural network module to the uncalibrated gaze information and the display content to generate a user-specific gaze function (1206). The user-specific gaze function has one or more personalized parameters. And the neural network module can then apply the user-specific gaze function to the uncalibrated gaze information to generate calibrated gaze information associated with the display content on the display screen (1208).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.

Abstract: Example embodiments relate to estimating depth information based on iris size. A computing system may obtain an image depicting a person and determine a facial mesh for a face of the person based on features of the face. In some instances, the facial mesh includes a combination of facial landmarks and eye landmarks. As such, the computing system may estimate an iris pixel dimension of an eye based on the eye landmarks of the facial mesh and estimate a distance of the eye of the face relative to the camera based on the iris pixel dimension, a mean value iris dimension, and an intrinsic matrix of the camera. The computing system may further modify the image based on the estimated distance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for gaze position prediction using neural networks. One of the systems includes a neural network comprising one or more neural network layers, wherein the neural network is configured to obtain a collection of input facial images of a user, wherein the collection of input facial images of the user comprises (i) a query image of the user, (ii) one or more calibration images of the user, and (iii) a respective calibration label that labels a known gaze position of the user for each of the one or more calibration images of the user; and process the received collection of input facial images of the user using the one or more neural network layers to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for gaze position prediction using neural networks. One of the systems includes a neural network comprising one or more neural network layers, wherein the neural network is configured to obtain a collection of input facial images of a user, wherein the collection of input facial images of the user comprises (i) a query image of the user, (ii) one or more calibration images of the user, and (iii) a respective calibration label that labels a known gaze position of the user for each of the one or more calibration images of the user; and process the received collection of input facial images of the user using the one or more neural network layers to generate a neural network output that characterizes a gaze position of the user in the query image.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.

Abstract: Disclosed are methods for diagnosing declarative memory loss using mouse tracking to follow the visual gaze of a subject taking a visual paired comparison test. Also disclosed are methods for diagnosing dementia such as mild cognitive impairment and Alzheimer's disease.