Google Inc.

Vidhya Navalpakkam

Shanmugasundaram Ravikumar

Nachiappan Valliappan

Badih Ghazi

Junfeng He

Pasin Manurangsi

Dmitry Lagun

Kai Jochen Kohlhoff

Improved methods are provided for generating heatmaps or other summary map data from multiple users' data (e.g., probability distributions) in a manner that preserves the privacy of the users' data while also generating heatmaps that are visually similar to the 'true' heatmap. These methods include decomposing the average of the users' data (the ‘true’ heatmap) into multiple different spatial scales, injecting random noise into the data at the multiple different spatial scales, and then reconstructing the privacy-preserving heatmap based on the noisy multi-scale representations. The magnitude of the noise injected at each spatial scale is selected to ensure preservation of privacy while also resulting in heatmaps that are visually similar to the ‘true’ heatmap.

Differentially private heatmaps

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.

Eye gaze tracking using neural networks

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Santa Clara, CA, United States of America

Vidhya Navalpakkam