An Appearance based Method for Eye Gaze Tracking

Abstract

Problem statement: Gaze estimation systems compute the direction of eye gaze based on observed eye movements. The need for gaze-contingent applications is the basis of the current research work. The gaze pointing systems is a substitute for the existing input devices. Approach: The gaze tracking methods are either feature based or appearance based. In this study, an appearance based approach for gaze tracking is proposed based on Run Length Coding (RLC). The experiment was conducted considering transitional changes and the class-intervals in iris pixels. The image acquisition begins from the center of the screen in anticlockwise direction. The center of the screen was the pivot point. Results: Using RLC, the recognition rate of 95% was achieved. The image analysis in different directions determines the gaze point. The directions was determined with respect to the pivot point. Conclusion:The proposed system provides a robust, less computational gaze tracking method using web camera.

Focus on Area Tracking Based on Deep Learning for Multiple Users

“…With advances in information technology, appearancebased eye-tracking methods have been introduced [8]. Sheela and Vijaya [9], included one or more methods based on run-length coding (RLC) to improve an eye-tracking system. The accuracy of this system is as high as 95%.…”

Estimasi Arah Tatapan Mata Menggunakan Ensemble Convolutional Neural Network

“…Ada beberapa jenis pendekatan pelacakan arah tatapan mata. Ada pendekatan pelacakan arah tatapan mata yang berbasis fitur [5]- [9] dan juga ada yang berbasis tampilan [10] - [11] Telah ada beberapa karya terkait yang meneliti arah tatapan mata dengan menggunakan metode CNN, salah satunya adalah [12]. Pada paper tersebut menggunakan CNN untuk mengelolah mata kanan dan mata kiri secara independen dan membandingkan hasil recognition rate antara 3 dan 7 kelas.…”

“…Gambar 1. Skema dan Arsitektur yang Digunakan pada "Real-time Eye Gaze Direction Classification Using Convolutional Neural Network" [12].…”

“…Sebagai hasil pembanding, akan digunakan paper yang ditulis oleh A. George bersama dengan A. Routray yang berjudul "Real-time Eye Gaze Direction Classification Using Convolutional Neural Network" [12]. Pada penelitian tersebut dataset yang digunakan adalah Eye Chimera database [19], [20] yang mengandung tujuh kelas EAC (Eye Accessing Cues).…”

Eye tracking by genetic deformable template matching algorithm in video sequences

“…Eye detection and tracking is a fundamental task in computer vision, with broad applications in humancomputer interaction, behavioral analysis, and computer graphics, such as monitoring human vigilance [1], gazecontingent smart graphics [2], and assisting people with disability. Various methods for eye detection and tracking are reported in the literature and can be classified into three categories: Appearance based methods [3], Feature based methods [4], and template based methods [5, 6, and 7].Appearance based method detect eyes based on their photometric appearance and require a large amount of data for training the classifiers (e. g. neural network or the support vector machine). Feature-based methods explore the characteristics of the human eye to identify a set of distinctive features around the eyes.…”