Pupil Detection Algorithm Based on Feature Extraction for Eye Gaze
S. Deivanayagi1, V. G. Nandhini Sri2, P. Kalai Priya3, G. Aarthi4
1S. Deivanayagi, Associate Professor, Department of ECE, Sri Sairam Institute of Technology, Chennai (Tamil Nadu), India.
2V. G. Nandhini Sri, Department of ECE, Sri Sairam Institute of Technology, Chennai (Tamil Nadu), India.
3P. Kalai Priya, Department of ECE, Sri Sairam Institute of Technology, Chennai (Tamil Nadu), India.
4G. Aarthi, Final Year Students, Department of ECE, Sri Sairam Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 14 July 2019 | Revised Manuscript received on 10 August 2019 | Manuscript Published on 29 August 2019 | PP: 73-76 | Volume-8 Issue-2S5 July 2019 | Retrieval Number: B10160682S519/2019©BEIESP | DOI: 10.35940/ijrte.B1016.0782S519
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Exact real-time pupil tracking is an essential step in a live eye gaze. Since pupil centre is a base point’s reference, eye centre localization is essential for many applications. In this research, we extract pupil eye features exactly within different intensity levels of eye images, mostly with localization of determined interest objects and where the human is looking for. Since it’s a digital world and digital transformation, everything is becoming virtual. Hence this concept has a huge scope in e-learning, class room training and analyzing human behaviour. This research covers eye tracking technology to track and analyze the learners’ behavior and emotion on e-learning platform like level of attention and tiredness. Harr’s cascade classifier was used to first locate the eye’s area, and once found and support vector machine (SVM) for classification with the trained datasets. We also include the state of emotions, facial landmarks of the salient patches on face image using automated learning-free facial landmark detection technique.Experimental results help in developing learner eye gaze detection in system using Pycharm and hardware output using Raspberry Pi. In Raspberry Pi is given with the input image captured using external webcam and based on the engagement level of the learner content 1 or 2 would be displayed in the Raspbian OS environment.
Keywords: Image Processing, SVM, Harr’s Cascade.
Scope of the Article: Algorithm Engineering