Unsupervised Blink Detection Using Eye Aspect Ratio Values Article Swipe
Bharana Fernando
,
Arjun Sridhar
,
Shawhin Talebi
,
John Waczak
,
David J. Lary
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202203.0200.v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202203.0200.v1
The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202203.0200.v1
- https://www.preprints.org/manuscript/202203.0200/v1/download
- OA Status
- green
- Cited By
- 2
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221058320
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221058320Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202203.0200.v1Digital Object Identifier
- Title
-
Unsupervised Blink Detection Using Eye Aspect Ratio ValuesWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-03-15Full publication date if available
- Authors
-
Bharana Fernando, Arjun Sridhar, Shawhin Talebi, John Waczak, David J. LaryList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202203.0200.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/202203.0200/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.preprints.org/manuscript/202203.0200/v1/downloadDirect OA link when available
- Concepts
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Pupil size, Novelty, Computer science, Eye tracking, Artificial intelligence, Pupil, Novelty detection, Window (computing), Pattern recognition (psychology), Computer vision, Machine learning, Psychology, Social psychology, Operating system, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.work, | 51 |
| abstract_inverted_index.aspect | 28, 81 |
| abstract_inverted_index.mental | 106 |
| abstract_inverted_index.method | 67 |
| abstract_inverted_index.tools. | 48 |
| abstract_inverted_index.values | 83 |
| abstract_inverted_index.window | 5 |
| abstract_inverted_index.factors | 13 |
| abstract_inverted_index.machine | 57 |
| abstract_inverted_index.present | 53 |
| abstract_inverted_index.provide | 98 |
| abstract_inverted_index.status. | 107 |
| abstract_inverted_index.studied | 20 |
| abstract_inverted_index.Accurate | 87 |
| abstract_inverted_index.Although | 12 |
| abstract_inverted_index.compared | 41, 69 |
| abstract_inverted_index.existing | 63, 72, 93 |
| abstract_inverted_index.explored | 24 |
| abstract_inverted_index.learning | 58 |
| abstract_inverted_index.novelty, | 33 |
| abstract_inverted_index.physical | 8, 104 |
| abstract_inverted_index.research | 95 |
| abstract_inverted_index.strategy | 61 |
| abstract_inverted_index.available | 40 |
| abstract_inverted_index.blinking. | 30 |
| abstract_inverted_index.cognitive | 10 |
| abstract_inverted_index.detection | 35, 60, 91 |
| abstract_inverted_index.algorithms | 76 |
| abstract_inverted_index.detection. | 86 |
| abstract_inverted_index.estimation | 47 |
| abstract_inverted_index.processes. | 11 |
| abstract_inverted_index.techniques | 36 |
| abstract_inverted_index.underlying | 7 |
| abstract_inverted_index.complements | 92 |
| abstract_inverted_index.informative | 27, 101 |
| abstract_inverted_index.potentially | 26 |
| abstract_inverted_index.techniques. | 73 |
| abstract_inverted_index.technology. | 65 |
| abstract_inverted_index.extensively, | 21 |
| abstract_inverted_index.eye-tracking | 43, 64, 94 |
| abstract_inverted_index.unsupervised | 56 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.52617602 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |