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Optimization of Lacrimal Aspect Ratio for Explainable Eye Blinking

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 824))

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Abstract

Eye blinking has been studied extensively due to its wide range of potential applications. However, one under-researched field is the use of the wider lacrimal area for detection. This paper proposes a new eye blinking detection method using a novel lacrimal aspect ratio (LAR) strategy that utilises eyebrow movement and eyes. The proposed algorithm estimates facial landmarks using an automatic facial landmark detector to extract a single scalar quantity by using LAR and characterizing eye opening and closing, and to detect both partial and full blinking in each frame using a LAR threshold. We set three threshold values, –2.4 and –2.6, and –2.9, to detect blinks by each frame. Experimental results show that our approach successfully detects eye blinks and can outperform other state-of-the-art works. The utilization of LAR in detecting blinks and partial blinks demonstrates its potential to offer a novel and informative metric for researchers. This approach also opens up possibilities for further eye-related investigations, including the recognition of emotions. With its low dimensionality and easily understandable time domain features, LAR provides an effective pathway towards achieving these goals.

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References

  1. Adireddi, V.S., Boddeda, C.N.S.J., Kumpatla, D.S., Mantri, C.D., Reddy, B.D., Geetha, G., Thirupathi Rao, N., Bhattacharyya, D.:. Detection of eye blink using svm classifier. In: Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2022, pp. 171–178. Springer (2023)

    Google Scholar 

  2. Adireddi, V.S., Boddeda, C.N.S.J., Kumpatla, D.S., Mantri, C.D., Dinesh Reddy, B., Geetha, G., Thirupathi Rao, N., Bhattacharyya, D.: Detection of eye blink using svm classifier. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds.) Smart Technologies in Data Science and Communication, pp. 171–178. Springer Nature Singapore, Singapore (2023)

    Google Scholar 

  3. Akhdan, S.R., Supriyanti, R., Nugroho, A.S.: Face recognition with anti spoofing eye blink detection. In: AIP Conference Proceedings, vol. 2482, no. 1, pp. 020006 (2023)

    Google Scholar 

  4. Al-gawwam, S., Benaissa, M.: Robust eye blink detection based on eye landmarks and savitzky-golay filtering. Information 9(4), 93 (2018)

    Article  Google Scholar 

  5. Anas, E.R., Henriquez, P., Matuszewski, B.J., et al.: Online eye status detection in the wild with convolutional neural networks. In: VISIGRAPP (6: VISAPP), pp. 88–95 (2017)

    Google Scholar 

  6. Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R., Elena Lopez, M.: Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 7(1), 63–77 (2006)

    Google Scholar 

  7. Borza, D., Itu, R., Danescu, R.: In the eye of the deceiver: analyzing eye movements as a cue to deception. J. Imaging 4(10), 120 (2018)

    Article  Google Scholar 

  8. Chollet F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  9. Chu, C.-H., Feng, Y.-K.: Study of eye blinking to improve face recognition for screen unlock on mobile devices. J. Electric. Eng. Technol. 13(2), 953–960 (2018)

    Google Scholar 

  10. Cori, J.M., Turner, S., Westlake, J., Naqvi, A., Ftouni, S., Wilkinson, V., Vakulin, A., O’Donoghue, F.J., Howard, M.E.: Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: a pilot study. Sleep Health 7(5), 644–651 (2021)

    Article  Google Scholar 

  11. Cortacero, K., Fischer, T., Demiris, Y.: Rt-bene: a dataset and baselines for real-time blink estimation in natural environments. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019)

    Google Scholar 

  12. Cortacero, K., Fischer, T., Demiris, Y.: Rt-bene: a dataset and baselines for real-time blink estimation in natural environments. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (Oct 2019)

    Google Scholar 

  13. Dari, S., Epple, N., Protschky, V.: Unsupervised blink detection and driver drowsiness metrics on naturalistic driving data. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. IEEE (2020)

    Google Scholar 

  14. de Lima Medeiros, P.A., da Silva, G.V.S., dos Santos Fernandes, F.R., Sánchez-Gendriz, I., Castro Lins, H.W., da Silva Barros, D.M., Pinto Nagem, D.A., de Medeiros Valentim, R.A.: Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam. Expert Syst. Appl. 188, 116073 (2022)

    Google Scholar 

  15. Dewi, C., Chen, R.-C., Jiang, X., Hui, Yu.: Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks. PeerJ Comput. Sci. 8, e943 (2022)

    Article  Google Scholar 

  16. Drutarovsky, T., Fogelton, A.: Eye blink detection using variance of motion vectors. In: European Conference on Computer Vision, pp. 436–448. Springer (2014)

    Google Scholar 

  17. Fogelton, A., Benesova, W.: Eye blink detection based on motion vectors analysis. Comput. Vis. Image Underst. 148, 23–33 (2016)

    Article  Google Scholar 

  18. Ghaziuddin, N., Nassiri, A., Miles, J.H.: Catatonia in down syndrome; a treatable cause of regression. Neuropsychiatr. Dis. Treat. 11, 941 (2015)

    Google Scholar 

  19. Ghosh, R., Phadikar, S., Deb, N., Sinha, N., Das, P., Ghaderpour, E.: Automatic eye-blink and muscular artifact detection and removal from eeg signals using k-nearest neighbour classifier and long short-term memory networks. IEEE Sens. J. (2023)

    Google Scholar 

  20. Grice, S.J., Halit, H., Farroni, T., Baron-Cohen, S., Bolton, P., Johnson, M.H.: Neural correlates of eye-gaze detection in young children with autism. Cortex 41(3), 342–353 (2005)

    Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  23. Hutamaputra, W., Utaminingrum, F., Setia Budi, A.: Eye gaze for menu display selection on smart wheelchair using k-nearest neighbors method. In: AIP Conference Proceedings, vol. 2609, pp. 040009. AIP Publishing LLC (2023)

    Google Scholar 

  24. Hutamaputra, W., Utaminingrum, F., Setia Budi, A., Ogata, K.: Eyes gaze detection based on multiprocess of ratio parameters for smart wheelchair menu selection in different screen size. J. Vis. Commun. Image Represent. 103756 (2023)

    Google Scholar 

  25. Ibrahim, B.R., Khalifa, F.M., Zeebaree, S.R.M., Othman, N.A., Alkhayyat, A., Zebari, R.R., Sadeeq, M.A.M.: Embedded system for eye blink detection using machine learning technique. In: 2021 1st Babylon International Conference on Information Technology and Science (BICITS), pp. 58–62. IEEE (2021)

    Google Scholar 

  26. Isler, J.R., Pini, N., Lucchini, M., Shuffrey, L.C., Morales, S., Bowers, M.E., Leach, S.C., Sania, A., Wang, L., Condon, C., et al.: Longitudinal characterization of eeg power spectra during eyes open and eyes closed conditions in children. Psychophysiology, e14158 (2023)

    Google Scholar 

  27. Jang, J., Lew, H.: Blink index as a response predictor of blepharospasm to botulinum neurotoxin-a treatment. Brain Behav. 11(11), e2374 (2021)

    Article  Google Scholar 

  28. Jordan, A.A., Pegatoquet, A., Castagnetti, A., Raybaut, J., Coz, P.L.: Deep learning for eye blink detection implemented at the edge. IEEE Embed. Syst. Lett. 13(3), 130–133 (2020)

    Google Scholar 

  29. Kashkouli, M.B., Abdolalizadeh, P., Abolfathzadeh, N., Sianati, H., Sharepour, M., Hadi, Y.: Periorbital facial rejuvenation; applied anatomy and pre-operative assessment. J. Curr. Ophthalmol. 29(3), 154–168 (2017)

    Google Scholar 

  30. King, D.E.: Dlib-ml: A machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

    Google Scholar 

  31. Kraft, D., Hartmann, F., Bieber, G.: Camera-based blink detection using 3d-landmarks. In: Proceedings of the 7th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence, pp. 1–7 (2022)

    Google Scholar 

  32. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Google Scholar 

  33. Królak, A., Strumillo, P.: Eye-blink detection system for human-computer interaction. Univers. Access Inf. Soc. 11, 1–11 (2011)

    Google Scholar 

  34. Liang, R., Song, Q.: Blink detection and duration estimation by using adaptive threshold with considering individual difference. In: 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 1116–1121. IEEE (2021)

    Google Scholar 

  35. Mackert, A., Woyth, C., Flechtner, K.-M., Volz, H.-P.: Increased blink rate in drug-naive acute schizophrenic patients. Biol. Psychiat. 27(11), 1197–1202 (1990)

    Article  Google Scholar 

  36. Malaspina, D., Coleman, E., Goetz, R.R., Harkavy-Friedman, J., Corcoran, C., Amador, X., Yale, S., Gorman, J.M.: Odor identification, eye tracking and deficit syndrome schizophrenia. Biol. Psychiatry 51(10), 809–815 (2002)

    Google Scholar 

  37. Moharana, L., Das, N., Nayak, S., Routray, A.: Video based eye blink analysis for psychological state determination. Intell. Dec. Technol. (Preprint), 1–10 (2021)

    Google Scholar 

  38. Patel, B.C., Anderson, R.L.: Blepharospasm and related facial movement disorders. Curr. Opin. Ophthalmol. 6(5), 86–99 (1995)

    Google Scholar 

  39. Phuong, T.T., Hien, L.T., Vinh, N.D., et al.: An eye blink detection technique in video surveillance based on eye aspect ratio. In: 2022 24th International Conference on Advanced Communication Technology (ICACT), pp. 534–538. IEEE (2022)

    Google Scholar 

  40. Radlak, K., Smolka, B.: Blink detection based on the weighted gradient descriptor. In: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, pp. 691–700. Springer (2013)

    Google Scholar 

  41. Rahman, A., Sirshar, M., Khan, A.: Real time drowsiness detection using eye blink monitoring. In: 2015 National software engineering conference (NSEC), pp. 1–7. IEEE (2015)

    Google Scholar 

  42. Rakshita, R.: Communication through real-time video oculography using face landmark detection. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1094–1098. IEEE (2018)

    Google Scholar 

  43. Sangeetha, J.: Deep learning architecture for a real-time driver safety drowsiness detection system. In: Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era, pp. 29–41. IGI Global (2023)

    Google Scholar 

  44. Soleymani, M., Pantic, M., Pun, T.: Multimodal emotion recognition in response to videos. IEEE Trans. Affect. Comput. 3(2), 211–223 (2011)

    Article  Google Scholar 

  45. Soukupova, T., Cech, J.: Eye blink detection using facial landmarks. In: 21st computer vision winter workshop, Rimske Toplice, Slovenia (2016)

    Google Scholar 

  46. Sridharan, S., Soundar, S., et al.: Assistive technology to communicate through eye blinks-a deep learning approach. Int. J. Comput. Digit. Syst. 11(1), 831–839 (2022)

    Article  Google Scholar 

  47. Sugawara, E., Nikaido, H.: Properties of adeabc and adeijk efflux systems of acinetobacter baumannii compared with those of the acrab-tolc system of escherichia coli. Antimicrob. Agents Chemother. 58(12), 7250–7257 (2014)

    Article  Google Scholar 

  48. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9 (2015)

    Google Scholar 

  49. Wang, D, Amoozgar, B., Porco, T., Wang, Z., Lin, S.C.: Ethnic differences in lens parameters measured by ocular biometry in a cataract surgery population. PloS one 12(6), e0179836 (2017)

    Google Scholar 

  50. Wang, L., Ann Alexander, C.: Machine learning in big data. Int. J. Math. Eng. Manag. Sci. 1(2), 52–61 (2016)

    Google Scholar 

  51. Yi, Y., Zhang, H., Zhang, W., Yuan, Y., Li, C.: Fatigue working detection based on facial multi-feature fusion. IEEE Sens. J. (2023)

    Google Scholar 

  52. You, F., Li, X., Gong, Y., Wang, H., Li, H.: A real-time driving drowsiness detection algorithm with individual differences consideration. IEEE Access 7, 179396–179408 (2019)

    Article  Google Scholar 

  53. Yuli Cristanti, R., Sigit, R.,Harsono, T., Adelina, D.C., Nabilah, A., Anggraeni, N.P.: Eye gaze tracking to operate android-based communication helper application. In: 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), pp. 89–94 (2017)

    Google Scholar 

  54. Zhang, H., Wang, X., Ren, W., Noack, B.R., Liu, H.: Improving the reliability of gaze estimation through cross-dataset multi-task learning. In: 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS), pp. 202–206. IEEE (2022)

    Google Scholar 

  55. Zhao, C., Gao, Z., Wang, Q., Xiao, K., Mo, Z., Jamal Deen, M.: Fedsup: a communication-efficient federated learning fatigue driving behaviors supervision approach. Future Gener. Comput. Syst. 138, 52–60 (2023)

    Google Scholar 

  56. Zhuang, Z., Landsittel, D., Benson, S., Roberge, R., Shaffer, R.: Facial anthropometric differences among gender, ethnicity, and age groups. Ann. Occup. Hyg. 54(4), 391–402 (2010)

    Google Scholar 

  57. Zwaard, S., Boele, H.-J., Alers, H., Strydis, C., Lew-Williams, C., Al-Ars, Z.: Privacy-preserving object detection & localization using distributed machine learning: a case study of infant eyeblink conditioning (2020). arXiv:2010.07259

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Authors and Affiliations

  1. School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Misbah Ayoub & Haiyang Zhang

  2. Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland

    Andrew Abel

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  1. Misbah Ayoub
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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Ayoub, M., Abel, A., Zhang, H. (2024). Optimization of Lacrimal Aspect Ratio for Explainable Eye Blinking. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_13

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