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2017 GTC San Jose

S7551 - Deep Unconstrained Gaze Estimation with Synthetic Data

Session Speakers
Session Description

Gaze tracking in unconstrained conditions, including inside cars, is challenging where traditional gaze trackers fail. We've developed a CNN-based algorithm for unconstrained, head-pose- and subject-independent gaze tracking, which requires only consumer-quality color images of the eyes to determine gaze direction, and points along the boundary of the eye, pupil, and iris. We'll describe how we successfully trained the CNN with millions of synthetic photorealistic eye images, which we rendered on the NVIDIA GPU for a wide range of head poses, gaze directions, subjects, and illumination conditions. Among appearance-based gaze estimation techniques, our algorithm has best-in-class accuracy.


Additional Session Information
Intermediate
Talk
AI for In-Vehicle Applications Computer Vision and Machine Vision
Automotive Media & Entertainment Software
25 minutes
Session Schedule