Next Article in Journal
Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site
Next Article in Special Issue
Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach
Previous Article in Journal
Understanding Smartwatch Battery Utilization in the Wild
Previous Article in Special Issue
Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review
Open AccessFeature PaperArticle

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

1
Computer Science Department, Liverpool John Moores University, Liverpool L33AF, UK
2
School of Engineering, University of Central Lancashire, Preston PR12HE, UK
3
Computer Science Department, College of Engineering and Computer Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3785; https://doi.org/10.3390/s20133785
Received: 8 June 2020 / Revised: 27 June 2020 / Accepted: 4 July 2020 / Published: 6 July 2020
(This article belongs to the Special Issue Robotics, Sensors and Industry 4.0)
Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection. View Full-Text
Keywords: pupil detection; deep eye; iris detection; eye centre localisation; eye gaze; facial analysis; image convolution; machine intelligence; pupil segmentation pupil detection; deep eye; iris detection; eye centre localisation; eye gaze; facial analysis; image convolution; machine intelligence; pupil segmentation
Show Figures

Figure 1

MDPI and ACS Style

Khan, W.; Hussain, A.; Kuru, K.; Al-askar, H. Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision. Sensors 2020, 20, 3785. https://doi.org/10.3390/s20133785

AMA Style

Khan W, Hussain A, Kuru K, Al-askar H. Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision. Sensors. 2020; 20(13):3785. https://doi.org/10.3390/s20133785

Chicago/Turabian Style

Khan, Wasiq; Hussain, Abir; Kuru, Kaya; Al-askar, Haya. 2020. "Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision" Sensors 20, no. 13: 3785. https://doi.org/10.3390/s20133785

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop