A review of deep learning in blink detection
Blink detection is a highly concerned research direction in the field of computer vision, which plays a key role in various application scenes such as human-computer interaction, fatigue detection and emotion perception. In recent years, with the rapid development of deep learning, the application o...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2594.pdf |
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| author | Jianbin Xiong Weikun Dai Qi Wang Xiangjun Dong Baoyu Ye Jianxiang Yang |
| author_facet | Jianbin Xiong Weikun Dai Qi Wang Xiangjun Dong Baoyu Ye Jianxiang Yang |
| author_sort | Jianbin Xiong |
| collection | DOAJ |
| description | Blink detection is a highly concerned research direction in the field of computer vision, which plays a key role in various application scenes such as human-computer interaction, fatigue detection and emotion perception. In recent years, with the rapid development of deep learning, the application of deep learning techniques for precise blink detection has emerged as a significant area of interest among researchers. Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. However, the current research on blink detection based on deep learning lacks systematic summarization and comparison. Therefore, the aim of this article is to comprehensively review the research progress in deep learning-based blink detection methods and help researchers to have a clear understanding of the various approaches in this field. This article analyzes the progress made by several classical deep learning models in practical applications of eye blink detection while highlighting their respective strengths and weaknesses. Furthermore, it provides a comprehensive summary of commonly used datasets and evaluation metrics for blink detection. Finally, it discusses the challenges and future directions of deep learning for blink detection applications. Our analysis reveals that deep learning-based blink detection methods demonstrate strong performance in detection. However, they encounter several challenges, including training data imbalance, complex environment interference, real-time processing issues and application device limitations. By overcoming the challenges identified in this study, the application prospects of deep learning-based blink detection algorithms will be significantly enhanced. |
| format | Article |
| id | doaj-art-9fb087e620ff466294651e8755ac5b65 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-9fb087e620ff466294651e8755ac5b652025-01-16T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e259410.7717/peerj-cs.2594A review of deep learning in blink detectionJianbin Xiong0Weikun Dai1Qi Wang2Xiangjun Dong3Baoyu Ye4Jianxiang Yang5School of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, ChinaSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, ChinaSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, ChinaSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, ChinaSchool of Aircraft Maintenance Engineering, Guangzhou Civil Aviation College, Guangzhou, Guangdong, ChinaSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, ChinaBlink detection is a highly concerned research direction in the field of computer vision, which plays a key role in various application scenes such as human-computer interaction, fatigue detection and emotion perception. In recent years, with the rapid development of deep learning, the application of deep learning techniques for precise blink detection has emerged as a significant area of interest among researchers. Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. However, the current research on blink detection based on deep learning lacks systematic summarization and comparison. Therefore, the aim of this article is to comprehensively review the research progress in deep learning-based blink detection methods and help researchers to have a clear understanding of the various approaches in this field. This article analyzes the progress made by several classical deep learning models in practical applications of eye blink detection while highlighting their respective strengths and weaknesses. Furthermore, it provides a comprehensive summary of commonly used datasets and evaluation metrics for blink detection. Finally, it discusses the challenges and future directions of deep learning for blink detection applications. Our analysis reveals that deep learning-based blink detection methods demonstrate strong performance in detection. However, they encounter several challenges, including training data imbalance, complex environment interference, real-time processing issues and application device limitations. By overcoming the challenges identified in this study, the application prospects of deep learning-based blink detection algorithms will be significantly enhanced.https://peerj.com/articles/cs-2594.pdfBlink detectionDeep learningFeature extractionTransfer learning |
| spellingShingle | Jianbin Xiong Weikun Dai Qi Wang Xiangjun Dong Baoyu Ye Jianxiang Yang A review of deep learning in blink detection PeerJ Computer Science Blink detection Deep learning Feature extraction Transfer learning |
| title | A review of deep learning in blink detection |
| title_full | A review of deep learning in blink detection |
| title_fullStr | A review of deep learning in blink detection |
| title_full_unstemmed | A review of deep learning in blink detection |
| title_short | A review of deep learning in blink detection |
| title_sort | review of deep learning in blink detection |
| topic | Blink detection Deep learning Feature extraction Transfer learning |
| url | https://peerj.com/articles/cs-2594.pdf |
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