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A review of deep learning in blink detection
Jianbin Xiong1, Weikun Dai1, Qi Wang1
1School of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China.
Peerj. Computer Science
|February 3, 2025
Summary
Deep learning significantly improves eye blink detection accuracy for computer vision applications. Challenges like data imbalance and environmental interference remain, but overcoming them will enhance algorithm performance.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Eye blink detection is crucial for human-computer interaction, fatigue monitoring, and emotion recognition.
- Deep learning methods offer superior feature learning and accuracy compared to traditional approaches for blink detection.
Purpose of the Study:
- To systematically review and compare deep learning-based blink detection methods.
- To provide researchers with a clear understanding of current approaches, datasets, and evaluation metrics.
Main Methods:
- Analysis of classical deep learning models applied to eye blink detection.
- Review of commonly used datasets and performance evaluation metrics.
- Discussion of strengths, weaknesses, challenges, and future directions.
Main Results:
- Deep learning methods show strong performance in blink detection.
- Key challenges include imbalanced training data, environmental interference, real-time processing, and device limitations.
Conclusions:
- Deep learning-based blink detection is highly effective but faces practical implementation hurdles.
- Addressing identified challenges will significantly improve the application prospects of these algorithms.
