A review of deep learning in blink detection

作     者：Xiong, Jianbin Dai, Weikun Wang, Qi Dong, Xiangjun Ye, Baoyu Yang, Jianxiang 

作者机构：Guangdong Polytech Normal Univ Sch Automat Guangzhou Guangdong Peoples R China Guangzhou Civil Aviat Coll Sch Aircraft Maintenance Engn Guangzhou Guangdong Peoples R China 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2025年第11卷

页      面：e2594-e2594页

核心收录：

基　　金：National Natural Science Foundation of China-Guangdong Province Joint fund key project [U22A20221] National Natural Science Foundation project Guangdong Province university key field special [2020ZDZX2014] Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province [PDJH2024A225] 

主　　题：Blink detection Deep learning Feature extraction Transfer learning 

摘      要：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.