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Khan, W, Hussain, A, Kuru, K and Al-askar, H (2020) Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision. Sensors, 20 (13). ISSN 1424-8220
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Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
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. We then 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 for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along-with a standardized distance metric we introduce first time in this study. 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.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | MDPI |
| Date Deposited: | 06 Jul 2020 09:24 |
| Last Modified: | 19 Aug 2022 11:00 |
| DOI or ID number: | 10.3390/s20133785 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/13258 |
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