Journal Article2020Cited by 6

Use of spontaneous blinking for application in human authentication

Amir Jalilifard, Dehua Chen, Aunnoy K Mutasim, M Raihanul Bashar, Rayhan Sardar Tipu, Ahsan-Ul Kabir Shawon, Nazmus Sakib, M Ashraful Amin, Md Kafiul Islam

Engineering Science and Technology, an International Journal

Elsevier, Vol. 23, Issue 4, pp. 903–910, ISBN: 2215-0986

DOI: 10.1016/j.jestch.2020.05.007Open AccessURL

Abstract

Contamination of electroencephalogram (EEG) signals due to natural blinking electrooculogram (EOG) signals is often removed to enhance the quality of EEG signals. This paper discusses the possibility of using solely involuntary blinking signals for human authentication. The EEG data of 46 subjects were recorded while the subject was looking at a sequence of different pictures. During the experiment, the subject was not focused on any kind of blinking task. Having the blink EOG signals separated from EEG, 25 features were extracted and the data were preprocessed in order to handle the corrupt or missing values. Since spontaneous and voluntary blinks have different characteristics in terms of kinematic variables and because the previous studies’ control setup may have altered the type of blink from spontaneous to voluntary, a series of statistical analysis was carried out in order to inspect the changes in the multivariate probability distribution of data compared to the previous studies. Statistical significance shows that it is very likely that the blink features of both voluntary and involuntary blink signal are generated by Gaussian probability density function, although different than voluntary blink, spontaneous blink is not well discriminated with Gaussian. Despite testing several models, none managed to classify the data using only the information of a single spontaneous blink. Thereby, we examined the possibility of learning the patterns of a series of blinks using Gated Recurrent Unit (GRU). Our results show that individuals can be distinguished with up to 98.7% accuracy using only a reasonably short sequence of involuntary blinking signals.

Keywords

  • gated recurrent unit
  • spontaneous blinking
  • electrooculogram
  • biometric identification
  • user authentication
  • Gaussian probability density

CCDS Authors

Nazmus Sakib
Nazmus Sakib

Nazmus Sakib

  • Co-Supervisor, BISPL Wing
  • Senior Research Associate, BISPL Wing

Adjunct Lecturer

Dept. of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Biomedical Signal Processing, Neural Signal Processing, Brain Computer Interface, Biomedical

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Md Kafiul Islam, PhD
Md Kafiul Islam, PhD

Md Kafiul Islam, PhD

  • Director, BISPL Wing

Professor

Dept. of Electrical and Electronic Engineering

Independent University, Bangladesh (IUB)

Published at

ICPR, IEEE Access

References

  1. 1.Leo Breiman. (2001). Random Forests. Machine Learning, 45(1), 5–32[10.1023/a:1010933404324]
  2. 2.Sepp Hochreiter, Jürgen Schmidhuber. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780[10.1162/neco.1997.9.8.1735]
  3. 3.Onur Teymur, Kostas Zygalakis, Ben Calderhead. (2016). Advances in Neural Information Processing Systems 29.
  4. 4.Hubert W. Lilliefors. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399[10.2307/2283970]
  5. 5.Hubert W. Lilliefors. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399–402[10.1080/01621459.1967.10482916]
  6. 6.Tsutomu Matsumoto, Hiroyuki Matsumoto, Kôji Yamada, Satoshi Hoshino. (2002). <title>Impact of artificial "gummy" fingers on fingerprint systems</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 4677, 275–289[10.1117/12.462719]
  7. 7.Edgar Acuña, Caroline Rodriguez. (2004). The Treatment of Missing Values and its Effect on Classifier Accuracy. , 639–647[10.1007/978-3-642-17103-1_60]
  8. 8.Anna Rita Bentivoglio, Susan Bressman, Emanuele Cassetta, Donatella Carretta, P. Tonali, Alberto Albanese. (1997). Analysis of blink rate patterns in normal subjects. Movement Disorders, 12(6), 1028–1034[10.1002/mds.870120629]
  9. 9.Reeju Pokharel, Sohan Seth, J.C. Principe. (2011). Neural Networks (IJCNN), The 2011 International Joint Conference on. CiteSeer X (The Pennsylvania State University)[link]
  10. 10.David Banks, Frederick R. McMorris, Phipps Arabie, Wolfgang Gaul. (2004). Classification, Clustering, and Data Mining Applications. [10.1007/978-3-642-17103-1]
  11. 11.Rahul Dey, Fathi M. Salem. (2017). Gate-variants of Gated Recurrent Unit (GRU) neural networks. , 1597–1600[10.1109/mwscas.2017.8053243]
  12. 12.Marios Poulos, Maria Rangoussi, V. Chrissikopoulos, A. Evangelou. (2003). Person identification based on parametric processing of the EEG. , 1, 283–286[10.1109/icecs.1999.812278]
  13. 13.Tsutomu Matsumoto, Hiroyuki Matsumoto, Kôji Yamada, Satoshi Hoshino. (2002). Impact of Artificial "Gummy" Fingers on Fingerprint Systems.. Datenschutz Und Datensicherheit, 26
  14. 14.Marie T. Balaban, Heather N. Taussig. (1994). Salience of fear/threat in the affective modulation of the human startle blink. Biological Psychology, 38(2-3), 117–131[10.1016/0301-0511(94)90033-7]
  15. 15.Mohammed Abo‐Zahhad, Sabah M. Ahmed, Sherif N. Abbas. (2015). A new multi-level approach to EEG based human authentication using eye blinking. Pattern Recognition Letters, 82, 216–225[10.1016/j.patrec.2015.07.034]
  16. 16.Kelly Kleifges, Nima Bigdely-Shamlo, Scott E. Kerick, Kay A. Robbins. (2017). BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis. Frontiers in Neuroscience, 11, 12[10.3389/fnins.2017.00012]
  17. 17.Qunjian Wu, Ying Zeng, Chi Zhang, Li Tong, Bin Yan. (2018). An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals. Sensors, 18(2), 335[10.3390/s18020335]
  18. 18.International Conference on Computational Intelligence in Pattern Recognition 2019 Hāora, Asit Kumar Das, Janmenjoy Nayak, Bighnaraj Naik, Soumen Kumar Pati, Danilo Pelusi. (2019). Computational Intelligence in Pattern Recognition. Advances in intelligent systems and computing[10.1007/978-981-13-9042-5]
  19. 19.Matteo Bologna, Rocco Agostino, B. Gregori, Daniele Belvisi, Donatella Ottaviani, Carlo Colosimo, Giovanni Fabbrini, Alfredo Berardelli. (2008). Voluntary, spontaneous and reflex blinking in patients with clinically probable progressive supranuclear palsy. Brain, 132(2), 502–510[10.1093/brain/awn317]
  20. 20.Mohammed Abo‐Zahhad, Sabah M. Ahmed, Sherif N. Abbas. (2014). A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal. IEEE Signal Processing Letters, 22(7), 876–880[10.1109/lsp.2014.2374338]
  21. 21.Javier Galbally, Riccardo Satta. (2015). Three-dimensional and two-and-a-half-dimensional face recognition spoofing using three-dimensional printed models. IET Biometrics, 5(2), 83–91[10.1049/iet-bmt.2014.0075]
  22. 22.Tien Pham, Wanli Ma, Dat Tran, Phuoc Nguyen, Dinh Phung. (2014). Multi-factor EEG-based user authentication. , 4029–4034[10.1109/ijcnn.2014.6889569]
  23. 23.Abdelkader Nasreddine Belkacem, Duk Shin, Hiroyuki Kambara, Natsue Yoshimura, Yasuharu Koike. (2014). Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors. Biomedical Signal Processing and Control, 16, 40–47[10.1016/j.bspc.2014.10.005]
  24. 24.Richard Jiang, Somaya Al-Máadeed, Ahmed Bouridane, Danny Crookes, Azeddine Beghdadi. (2016). Biometric Security and Privacy. Signal processing for security technologies[10.1007/978-3-319-47301-7]
  25. 25.Abdelkader Nasreddine Belkacem. (2013). Journal of Medical and Biological Engineering[10.5405/jmbe.1596]
  26. 26.Sherif Nagib Abbas Seha, Georgios Papangelakis, Dimitrios Hatzinakos, Ali Shahidi Zandi, Felix J. E. Comeau. (2019). Improving Eye Movement Biometrics Using Remote Registration of Eye Blinking Patterns. , 2562–2566[10.1109/icassp.2019.8683757]
  27. 27.V Březinová, R. E. Kendell. (1977). Smooth Pursuit Eye Movements of Schizophrenics and Normal People Under Stress. The British Journal of Psychiatry, 130(1), 59–63[10.1192/bjp.130.1.59]
  28. 28.Sherif N. Abbas, Mohammed Abo‐Zahhad. (2016). Eye Blinking EOG Signals as Biometrics. Signal processing for security technologies, 121–140[10.1007/978-3-319-47301-7_5]
  29. 29.Julián Espinosa, Begoña Doménech, Carmen Vázquez, Jorge Pérez, David Mas. (2018). Blinking characterization from high speed video records. Application to biometric authentication. PLoS ONE, 13(5), e0196125[10.1371/journal.pone.0196125]
  30. 30.Isabella Araújo Mota, Otávio Gomes Lins. (2016). Bereitschaftspotential preceding spontaneous and voluntary eyelid blinks in normal individuals. Clinical Neurophysiology, 128(1), 100–105[10.1016/j.clinph.2016.10.010]
  31. 31.Aunnoy K Mutasim, Rayhan Sardar Tipu, Mohammad Raihanul Bashar, Md. Kafiul Islam, M. Ashraful Amin. (2018). Computational Intelligence for Pattern Recognition in EEG Signals. Studies in computational intelligence, 291–320[10.1007/978-3-319-89629-8_11]
  32. 32.Scott Lundberg, Su‐In Lee. (2017). A Unified Approach to Interpreting Model Predictions. arXiv (Cornell University)[10.48550/arxiv.1705.07874]
  33. 33.(2016). 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS). [10.1109/mwscas38648.2016]

Cited By

  1. 1.Christos A. Fidas, Dimitrios Lyras. (2023). A Review of EEG-Based User Authentication: Trends and Future Research Directions. IEEE Access, 11, 22917–22934[10.1109/access.2023.3253026]
  2. 2.Kutlucan Gorur. (2023). Fourier Synchrosqueezing Transform-ICA-EMD Framework Based EOG-Biometric Sustainable and Continuous Authentication via Voluntary Eye Blinking Activities. Biomimetics, 8(4), 378[10.3390/biomimetics8040378]
  3. 3.Шибельгут Нонна Марковна, Батина Наталья Анатольевна, Мозес Вадим Гельевич, Рудаева Елена Владимировна, Елгина Светлана Ивановна, Мозес Кира Борисовна. (2025). КЛИНИЧЕСКИЙ СЛУЧАЙ ВРАСТАНИЯ ПЛАЦЕНТЫ. Matʹ i ditâ v Kuzbasse[10.24412/2686-7338-2025-1-92-95]
  4. 4.YueFang Dong, HaiLong Zhu, Zhe Zhou, Weiwei Fu. (2023). Fatigue Monitoring and Awakening System Based on Eye Electrical and Head Movement Parameters Monitoring. , 174–178[10.1109/ictech58362.2023.00044]
  5. 5.B. Kaushik, M Vinay Kumar, Sirisha Tadepalli, Ashish Vats, R Bhargav. (2023). EOG-based Blink Authentication. , 1–5[10.1109/icraset59632.2023.10420107]
  6. 6.David Escobar-Valencia, Fernando Jesús Regino-Ubarnes, Diana Yamileth Velásquez-Maldonado. (2022). Señales EOG: una revisión sobre procesamiento y aplicaciones. Mundo FESC, 12(23), 173–187[10.61799/2216-0388.1206]
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