Eye Pupil Localisation and Labeling Using a Small Size Database and YOLOv4 Object Detection Algorithm

Varuzhan H. Baghdasaryan

Abstract

Eye-related research has shown that eye gaze data are very important for applications that are essential to human daily life. Eye gaze data has been used in research and systems for eye movements, eye tracking and eye gaze tracking. Eye pupil localization, labelling and tracking are challenging problems in computer science. This article discusses and explores that problem․ The YOLOv4 (“You only look once”) object detection algorithm which is an evolution of the YOLOv3 model has been evaluated in a tiny database consisting of 103 eye images. The YOLOv4 algorithm was created by Alexey Bochkovskiy, Chien-Yao Wang and Hong-Yuan Mark Liao [3]. It is twice as fast as EfficientDet with comparable performance. The main purpose of this article is to test the YOLOv4 algorithm, to find out its effectiveness in process of localization and labelling of eye pupils and find out (determine) the effectiveness of the algorithm when training with a tiny database and with a relatively small number of iterations.

Keywords

eye pupil localization; eye pupil labelling; neural network; YOLOv4; tiny database; a small number of iterations.

Cite This Article

Baghdasaryan, V. H. (2022). Eye Pupil Localisation and Labeling Using a Small Size Database and YOLOv4 Object Detection Algorithm. International Journal of Scientific Advances (IJSCIA), Volume 3| Issue 5: Sep-Oct 2022, Pages 800-803, URL: https://www.ijscia.com/wp-content/uploads/2022/10/Volume3-Issue5-Sep-Oct-No.354-800-803.pdf

Volume 3 | Issue 5: Sep-Oct 2022