Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Young-Joo | - |
dc.contributor.author | Kim, Wooseong | - |
dc.contributor.author | Park, Joon-Sang | - |
dc.date.available | 2020-07-10T04:40:45Z | - |
dc.date.created | 2020-07-06 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1574-017X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/4787 | - |
dc.description.abstract | We propose an efficient method that can be used for eye-blinking detection or eye tracking on smartphone platforms in this paper. Eye-blinking detection or eye-tracking algorithms have various applications in mobile environments, for example, a countermeasure against spoofing in face recognition systems. In resource limited smartphone environments, one of the key issues of the eye-blinking detection problem is its computational efficiency. To tackle the problem, we take a hybrid approach combining two machine learning techniques: SVM (support vector machine) and CNN (convolutional neural network) such that the eye-blinking detection can be performed efficiently and reliably on resource-limited smartphones. Experimental results on commodity smartphones show that our approach achieves a precision of 94.4% and a processing rate of 22 frames per second. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.subject | FEATURE-EXTRACTION | - |
dc.subject | TRACKING | - |
dc.title | Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Joon-Sang | - |
dc.identifier.doi | 10.1155/2018/6929762 | - |
dc.identifier.scopusid | 2-s2.0-85048106877 | - |
dc.identifier.wosid | 000434212400001 | - |
dc.identifier.bibliographicCitation | MOBILE INFORMATION SYSTEMS, v.2018 | - |
dc.relation.isPartOf | MOBILE INFORMATION SYSTEMS | - |
dc.citation.title | MOBILE INFORMATION SYSTEMS | - |
dc.citation.volume | 2018 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | FEATURE-EXTRACTION | - |
dc.subject.keywordPlus | TRACKING | - |
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