EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET

Authors

  • Eswin Syahputra Universitas Pembangunan Panca Budi Medan
  • Irpan Nursukmi Universitas Pembangunan Panca Budi Medan
  • Sony Putra Universitas Pembangunan Panca Budi Medan
  • Bayu Sukma Sani Universitas Pembangunan Panca Budi Medan
  • Rian Farta Wijaya Universitas Pembangunan Panca Budi Medan

DOI:

https://doi.org/10.30829/zero.v6i2.14751

Keywords:

Blink Detections, Eye Aspect Ratio, Eye Blink, Facial Landmarks, Dlib

Abstract

Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing. However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques.

References

Al Tawil L, Aldokhayel S, Zeitouni L, Qadoumi T, Hussein S, Ahamed SS. 2020. Prevalence of self-reported computer vision syndrome symptoms and its associated factors among university students. European Journal of Ophthalmology 30(1):189-195 DOI 10.1177/1120672118815110.

Čech J, Franc V, Uřičář M, Matas J. 2016. Multi-view facial landmark detection using a 3D shape model. Image and Vision Computing 47:60-70 DOI 10.1016/j.imavis.2015.11.003.

Dhiraj, Jain DK. 2019. An evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery. Pattern Recognition Letters 120:112-119 DOI 10.1016/j.patrec.2019.01.014.

Divjak M, Bischof H. 2009. Eye blink based fatigue detection for prevention of computer vision syndrome. In: Proceedings of the 11th IAPR conference on machine vision applications, MVA 2009.

Fatima B, Shahid AR, Ziauddin S, Safi AA, Ramzan H. 2020. Driver fatigue detection using viola jones and principal component analysis. Applied Artificial Intelligence 34(6):456-483 DOI 10.1080/08839514.2020.1723875.

King DE. 2009. Dlib-ml: a machine learning toolkit. Journal of Machine Learning Research 10:1755-1758.

Królak A, Strumiłło P. 2012. Eye-blink detection system for human-computer interaction. Universal Access in the Information Society 11(4):409-419 DOI 10.1007/s10209-011-0256-6.

Pan G, Sun L, Wu Z, Lao S. 2007. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Proceedings of the IEEE international conference on computer vision. Piscataway: IEEE, 1-8 DOI 10.1109/ICCV.2007.4409068.

Rahman A, Sirshar M, Khan A. 2016. Real time drowsiness detection using eye blink monitoring. In: 2015 National software engineering conference, NSEC, 2015. 1-7 DOI 10.1109/NSEC.2015.7396336.

Rosenfield M. 2011. Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic and Physiological Optics 31(5):502-515 DOI 10.1111/j.1475-1313.2011.00834.x.

Wilson GF. 2002. An analysis of mental workload in pilots during flight using multiple psychophysiological measures. International Journal of Aviation Psychology 12(1 SPEC):3-18 DOI 10.1207/s15327108ijap1201_2.

Wu Y, Ji Q. 2019. Facial landmark detection: a literature survey. International Journal of Computer Vision 127(2):115-142 DOI 10.1007/s11263-018-1097-z.

Yin S, Wang S, Chen X, Chen E, Liang C. 2020. Attentive one-dimensional heatmap regression for facial landmark detection and tracking. In: MM 2020 - Proceedings of the 28th ACM international conference on multimedia. New York: ACM, DOI 10.1145/3394171.3413509.

Downloads

Published

2023-02-10

Issue

Vol. 6 No. 2 (2022): Zero: Jurnal Sains Matematika dan Terapan

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).