Deep Neural Networks for Low-Cost Eye Tracking | Zendy

Ildar Rakhmatulin | Zendy; Andrew T. Duchowski | Zendy

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Deep Neural Networks for Low-Cost Eye Tracking

Author(s) -

Ildar Rakhmatulin,

Andrew T. Duchowski

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.09.041

Subject(s) - gaze , computer science , artificial intelligence , computer vision , eye tracking , python (programming language) , artificial neural network , deep learning , tracking (education) , process (computing) , operating system , psychology , pedagogy

The paper presents a detailed analysis of modern techniques that can be used to track gaze with a webcam. We present a practical implementation of the most popular methods for tracking gaze. Various models of deep neural networks that can be involved in the process of online gaze monitoring are reviewed. We introduce a new eye-tracking approach where the effectiveness of using a deep learning method is significantly increased. Implementation is in Python where its application is demonstrated by controlling interaction with the computer. Specifically, a dual coordinate system is given for controlling the computer with the help of a gaze. The first set of coordinates-the position of the face relative to the computer, is implemented by detecting color from the infrared LED via the OpenCV library. The second set of coordinates-giving gaze position-is obtained via the YOLO (v3) package. A method of labeling the eyes is given, in which 3 objects are used to track gaze (to the left, to the right, and in the center).

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