[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123640-en":3,"doc-seo-123640-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123640,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","A MACHINE LEARNING APPROACH TO EYE BLINK DETECTION IN LOW-LIGHT VIDEOS","Inadequate lighting conditions degrade the reliability of eye blink detection systems that support fatigue detection, transportation, and security applications. Although some capture devices use flashlight technology, occasional user omission can produce slightly darker videos and harder visual conditions. This study presents a machine learning-based blink detection system designed to identify blinks and flash-related cues in low-light footage. Performance is assessed with a confusion matrix using 31 short videos, and experiments report 100% accuracy. Additional work is needed for more realistic, diverse scenarios.","A MACHINE LEARNING APPROACH TO EYE BLINK DETECTION IN LOW-LIGHT  \nVIDEOS  \nMuhammad Furqan Rasyid*1, Muhammad Rizal2, Wilem Musu3, Muhammad Sabirin Hadis4  \n1Department of Informatics Management, Dipa Makassar University, Indonesia  \n2,3Department of Informatics Engineering, Dipa Makassar University, Indonesia 4Department of Electrical Engineering and Computer Science, Kanazawa University, Kanazawa, Japan [Email:](Email:1muhammad.furqan@undipa.ac.id)[1](Email:1muhammad.furqan@undipa.ac.id)[muhammad.furqan@undipa.ac.id](Email:1muhammad.furqan@undipa.ac.id), [2](2muhammad.rizal@undipa.ac.id)[muhammad.rizal@undipa.ac.id](2muhammad.rizal@undipa.ac.id), [3](3wilem.musu@undipa.ac.id)[wilem.musu@undipa.ac.id](3wilem.musu@undipa.ac.id),  \n[4](4sabirinhadis@stu.kanazawa-u.ac.jp)[sabirinhadis@stu.kanazawa-u.ac.jp](4sabirinhadis@stu.kanazawa-u.ac.jp)  \n(Article received: May 02, 2023; Revision: May 23, 2023; published: June 26, 2023)  \nAbstract  \nInadequate lighting conditions can harm the accuracy of blink detection systems, which play a crucial role in fatigue detection technology, transportation and security applications. While some video capture devices are now equipped with flashlight technology to enhance lighting, users occasionally need to remember to activate this feature, resulting in slightly darker videos. Consequently, there is a pressing need to improve the performance of blink detection systems to detect eye accurately blinks in low light videos. This research proposes developing a machine learning-based blink detection system to see flashes in low-light videos. The Confusion matrix was conducted to evaluate the effectiveness of the proposed blink detection system. These tests involved 31 videos ranging from 5 to 10 seconds in duration. Involving male and female test subjects aged between 20 and 22. The accuracy of the proposed blink detection system was measured using the confusion matrix method. The results indicate that by leveraging a machine learning approach, the blink detection system achieved a remarkable accuracy of 100% in detecting blinks within low-light videos. However, this research necessitates further development to account for more complex and diverse real-life situations. Future studies couldfocus on developing more sophisticated algorithms and expanding the test subjects to improve the performance of the blink detection system in low light conditions. Such advancements would contribute to the practical application of the system ina broader range of scenarios, ultimately enhancing its effectiveness in fatigue detection technology.  \nKeywords: confusion matrix, eye blink detection, flashlight, low-light video, machine learning.  \n1. INTRODUCTION  \nIn today's digital era, video has become one of the most popular and widely used media in various fields, such as industry, entertainment, and research [1]. Video is also often used in medical testing to seethe patient's condition. However, the main problem with video analysis is that some videos still have qualities that complicate the data analysis process. Many tasks can be accomplished with video [2] . Oneof these videos is detecting the blink of an eye in a subject, which can provide important information in medical research and diagnosis. Catching eye blinksin low-light videos is difficult because many factors, such as video quality, lighting, camera position, and subject movement, can affect the analysis results. Therefore, it requires a proper and sophisticated approach to solve the problem.  \nSeveral previous studies detect eyes blinking. Research conducted by [3] notices eyes blinking using the EAR (Eyes Aspect Ratio) method with an accuracy of up to 97%. The study by [4] introduces anew unsupervised learning algorithm that combines thresholding and a mixed Gaussian model (GMM) to achieve precise and effective eye blink detection. The  \nproposed algorithm outperforms other recent methods regarding detection precision and F1 score. Research by [5] used s","cbCairV5d1g0xH4O","https://ap.wps.com/l/cbCairV5d1g0xH4O","pdf",469520,1,"English","en",105,"# Introduction\n## Related Work\n## Machine Learning for Blink Detection\n# Abstract\n# Keywords","[{\"question\":\"Why is blink detection difficult in low-light videos?\",\"answer\":\"Low light reduces visual clarity, and factors such as video quality, camera position, and subject movement further complicate analysis.\"},{\"question\":\"How does the proposed system evaluate blink detection performance?\",\"answer\":\"Effectiveness is evaluated using a confusion matrix computed from test results.\"},{\"question\":\"What accuracy did the experiments report for low-light blink detection?\",\"answer\":\"The reported accuracy for detecting blinks in low-light videos is 100% based on the confusion matrix results.\"}]","A MACHINE LEARNING APPROACH TO EYE BLINK DETECTION IN LOW-LIGHT VIDEOS | PDF",1785817782,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"a-machine-learning-approach-to-eye-blink-detection-in-low-light-videos","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-machine-learning-approach-to-eye-blink-detection-in-low-light-videos/123640/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is blink detection difficult in low-light videos?","Question",{"text":74,"@type":75},"Low light reduces visual clarity, and factors such as video quality, camera position, and subject movement further complicate analysis.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed system evaluate blink detection performance?",{"text":79,"@type":75},"Effectiveness is evaluated using a confusion matrix computed from test results.",{"name":81,"@type":72,"acceptedAnswer":82},"What accuracy did the experiments report for low-light blink detection?",{"text":83,"@type":75},"The reported accuracy for detecting blinks in low-light videos is 100% based on the confusion matrix results.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]