Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 27, Issue 1, Pages 190-203Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2019.2938165
Keywords
Three-dimensional displays; Gaze tracking; Iris; Cameras; Convolutional neural nets; Image reconstruction; Videos; 3D eye gaze tracking; convolutional neural network; facial capture
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Funding
- Natural Science Foundation of Beijing Municipality [L182052]
- National Natural Science Foundation of China [61772499]
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This paper proposes a realtime and accurate method for 3D eye gaze tracking using deep convolutional neural networks, which combines training efficiency and tracking accuracy in various conditions, advancing the state of the art in single RGB camera eye tracking.
This paper presents a realtime and accurate method for 3D eye gaze tracking with a monocular RGB camera. Our key idea is to train a deep convolutional neural network(DCNN) that automatically extracts the iris and pupil pixels of each eye from input images. To achieve this goal, we combine the power of Unet [1] and Squeezenet [2] to train an efficient convolutional neural network for pixel classification. In addition, we track the 3D eye gaze state in the Maximum A Posteriori (MAP) framework, which sequentially searches for the most likely state of the 3D eye gaze at each frame. When eye blinking occurs, the eye gaze tracker can obtain an inaccurate result. We further extend the convolutional neural network for eye close detection in order to improve the robustness and accuracy of the eye gaze tracker. Our system runs in realtime on desktop PCs and smart phones. We have evaluated our system on live videos and Internet videos, and our results demonstrate that the system is robust and accurate for various genders, races, lighting conditions, poses, shapes and facial expressions. A comparison against Wang et al. [3] shows that our method advances the state of the art in 3D eye tracking using a single RGB camera.
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