Earticle

초록

영어

This study systematically analyzes the impact of face angle and lighting changes on eye state recognition technology and compares the performance of three technologies: CNN, MediaPipe, and Dlib. Specifically, the CNN-based approach utilizes a transfer learning model, Inception, to assess eye state recognition accuracy. With recent advancements in AI and computer vision technology, eye state recognition has become crucial in applications like driver drowsiness detection, user authentication, and medical monitoring. However, the performance of these technologies is greatly influenced by face angle and lighting conditions. This research evaluates the recognition accuracy of the three technologies under various face angles and lighting conditions, finding that CNN demonstrates robust performance against both lighting and angle variations. This study aims to provide fundamental data to improve the reliability of eye state recognition technology and to suggest future research directions.

목차

Abstract
1. Introduction
2. Related studies
2.1 Overview of Eye State Recognition Technology
2.2 Overview of CNN, MediaPipe, and Dlib
2.3 Impact of Face Angle and Lighting on Performance
3. Research Methods and Performance Analysis
3.1 Overview of Eye State Recognition Technology
3.2 Performance analysis
4. Comparative Analysis
5. Conclusion
Acknowledgement
References

키워드

저자

• Jung Min Park [ Student, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Dong Jun Hwang [ Student, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Yun Chang Hwang [ Student, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Ji Muk Lee [ Student, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Hyo Young Shin [ Professor, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Kye Dong Jung [ Visiting professor, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ]
• Cheol Young Go [ Adjunct professor, Department of Software Convergence, Namyangju Campus, Kyungbok University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380