Noise robustness evaluation of image processing algorithms for eye blinking detection
•Evaluation of robustness of different eye blinking algorithms.•Development of Low-cost imaging acquisition system and test protocol.•Montecarlo simulations to evaluate the algorithms performance with added Gaussian noise.•Algorithm based on Image correlation offers best performance with low noise l...
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| Published in | Measurement : journal of the International Measurement Confederation Vol. 239; p. 115508 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.01.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0263-2241 |
| DOI | 10.1016/j.measurement.2024.115508 |
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| Abstract | •Evaluation of robustness of different eye blinking algorithms.•Development of Low-cost imaging acquisition system and test protocol.•Montecarlo simulations to evaluate the algorithms performance with added Gaussian noise.•Algorithm based on Image correlation offers best performance with low noise level.•Algorithm based on Local binary pattern provides best results with high noise level.
Robust algorithms for eye blinking detection are required due to the effects of noisy environments and varying light conditions on image-based detection methods. This paper compares five non-supervised image-based algorithms for eye blinking detection, evaluating their robustness to additive Gaussian noise. The algorithms were tested on a video dataset acquired using a smartphone and an ophthalmology chin rest. Through Monte Carlo simulation that introduces Gaussian noise at different intensities, we evaluate the algorithms’ precision, sensitivity, and F1-scores for frame classification, True Positive Rate (TPR), False Discovery Rate (FDR) and F1-score for the event detector. The results of experimental tests reveal significant variations in algorithms’ performance with increasing noise levels. Notably, the Image Correlation (IC) algorithm demonstrates superior eye blinking detection capabilities under various noise conditions, emerging as the most robust algorithm among those tested. This distinction highlights the potential of IC for reliable blink detection in noisy environments. |
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| AbstractList | •Evaluation of robustness of different eye blinking algorithms.•Development of Low-cost imaging acquisition system and test protocol.•Montecarlo simulations to evaluate the algorithms performance with added Gaussian noise.•Algorithm based on Image correlation offers best performance with low noise level.•Algorithm based on Local binary pattern provides best results with high noise level.
Robust algorithms for eye blinking detection are required due to the effects of noisy environments and varying light conditions on image-based detection methods. This paper compares five non-supervised image-based algorithms for eye blinking detection, evaluating their robustness to additive Gaussian noise. The algorithms were tested on a video dataset acquired using a smartphone and an ophthalmology chin rest. Through Monte Carlo simulation that introduces Gaussian noise at different intensities, we evaluate the algorithms’ precision, sensitivity, and F1-scores for frame classification, True Positive Rate (TPR), False Discovery Rate (FDR) and F1-score for the event detector. The results of experimental tests reveal significant variations in algorithms’ performance with increasing noise levels. Notably, the Image Correlation (IC) algorithm demonstrates superior eye blinking detection capabilities under various noise conditions, emerging as the most robust algorithm among those tested. This distinction highlights the potential of IC for reliable blink detection in noisy environments. |
| ArticleNumber | 115508 |
| Author | Lanzolla, Anna Maria Lucia D’Alessandro, Vito Ivano Di Nisio, Attilio Attivissimo, Filippo Scarcelli, Giuliano |
| Author_xml | – sequence: 1 givenname: Attilio surname: Di Nisio fullname: Di Nisio, Attilio email: attilio.dinisio@poliba.it organization: Department of Electric and Information Engineering, Polytechnic University of Bari, Bari, Apulia, Italy – sequence: 2 givenname: Vito Ivano surname: D’Alessandro fullname: D’Alessandro, Vito Ivano email: v.dalessandro4@phd.poliba.it organization: Department of Electric and Information Engineering, Polytechnic University of Bari, Bari, Apulia, Italy – sequence: 3 givenname: Giuliano orcidid: 0000-0002-1736-077X surname: Scarcelli fullname: Scarcelli, Giuliano email: scarc@umd.edu organization: Fischell Department of Bioengineering, University of Maryland, USA – sequence: 4 givenname: Anna Maria Lucia surname: Lanzolla fullname: Lanzolla, Anna Maria Lucia email: anna.lanzolla@poliba.it organization: Department of Electric and Information Engineering, Polytechnic University of Bari, Bari, Apulia, Italy – sequence: 5 givenname: Filippo surname: Attivissimo fullname: Attivissimo, Filippo email: filippo.attivissimo@poliba.it organization: Department of Electric and Information Engineering, Polytechnic University of Bari, Bari, Apulia, Italy |
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| Keywords | Computer vision Robustness Ophthalmology Image processing Noise Eye blinking |
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