首页|Estimating 2D Gaze Coordinates from Efficiently Compressed Face Images
Estimating 2D Gaze Coordinates from Efficiently Compressed Face Images
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NSTL
Gaze tracking is an important tool in many domains. Recent development in Convolutional Neural Networks (CNN) has allowed invention of gaze tracking techniques that work on commodity hardware such as a camera on a personal computer. Moreover, it has been shown that information from the full-face region can provide better performance than from an eye image alone. However, the problem with using the full-face image is the heavy computation cost due to the large image size. This study tackles this problem by efficiently compressing face images using importance weights to face regions. It is shown that the image compressed with the proposed method preserves the accuracy than the image resized as it is.
2D Gaze EstimationEye TrackingCNNFace Images
Reo Ogusu、Takao Yamanaka
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Department of Information & Communication Sciences, Sophia University
NSTL
