Eye Gaze Capture for Preference Tracking in a Scalable Environment

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Author(s): G. Ananthi (Mepco Schlenk Engineering College, India), G. Shenbagalakshmi (Mepco Schlenk Engineering College, India), M. Pujaa (Mepco Schlenk Engineering College, India)and V.M. Amretha (Mepco Schlenk Engineering College, India)
Copyright: 2023
Pages: 16
Source title: Scalable and Distributed Machine Learning and Deep Learning Patterns
Source Author(s)/Editor(s): J. Joshua Thomas (UOW Malaysia KDU Penang University College, Malaysia), S. Harini (Vellore Institute of Technology, India)and V. Pattabiraman (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-9804-0.ch013

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Nowadays, eye gaze tracking of real-world people to acknowledge what they are seeing in a particular page is trending; but its complexity is also growing fast, and the accuracy is not enough. In the proposed system, the image patch of the eye region is extracted from the input image using the Viola Jones algorithm for facial feature detection. Then SqueezeNet and U-Net are combined to train the model for pixel classification of iris and pupil from the eye image patch with a training dataset that contains manually labelled iris and pupil region. After extracting the iris and pupil features, the eye gaze tracking is formulated by using 2D pupil center extracted by applying Mean-Shift algorithm and 3D eyeball center. The system achieved an accuracy of 99.93% which is best comparable to the state-of-the-art methods.

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Eye Gaze Capture for Preference Tracking in a Scalable Environment

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