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Effects of measurement time and presentation size conditions on biometric identification using eye movements

Published: 25 May 2021 Publication History

Abstract

Biometric identification using eye movements is an identification method with low risk of spoofing, however the problem with it is that the eye movement measurement time is long. In this paper, we studied pattern lock authentication using eye movement features. As a result of 1-to-N identification using the data of six subjects, it was found that the identification rate was maximized at a measurement time of 3 seconds, indicating that it was possible to identify individuals in a short measurement time. In addition, we examined the effects of the data measurement time conditions and the presentation size on the rate of identification. The condition which maximized the identification rate was a measurement time limit of 3 seconds or the presentation of a stimulus pattern using a visual angle of 27.20°. Furthermore, the Mel-Frequency Cepstral Coefficient (MFCC) of the viewpoint coordinates and the diameter of the pupil were the features that contributed most to identification.

References

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Nguyen Viet Cuong, Vu Dinh, and Lam Si Tung Ho. 2012. Mel-frequency Cepstral Coefficients for Eye Movement Identification. IEEE 24th International Conference on Tools with Artificial Intelligence (2012).
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Corey Holland and Oleg V. Komogortsev. 2011. Biometric Identification via Eye Movement Scanpaths in Reading. International Joint Conference on Biometrics (2011), 11–13.
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Pawel Kasprowski and Jozef Ober. 2004. Eye movements in biometrics. Lecture Notes in Computer Science 3087 (2004), 248–258.
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Christina Katsini, Yasmeen Abdrabou, George E. Raptis, Mohamed Khamis, and Florian Alt. 2020. The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions. CHI’20:Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–21.
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Tomi Kinnunen, Filip Sedlak, and Roman Bednarik. 2010. Towards task-independent person authentication using eye movement signals. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, Stephan N. Spencer (Ed.). ACM, ACM Press, New York, USA, 187–190.
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Tomasz Kocejko and Jerzy Wtorek. 2012. Gaze Pattern Lock for Elders and Disabled. Information Technologies in Biomedicine(2012), 589–602.
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Chandan Kumar, Daniyal Akbari, Raphael Menges, I Scott MacKenzie, and Steffen Staab. 2019. TouchGazePath:Multimodal Interaction with Touch and Gaze Path for Secure Yet Efficient PIN Entry. 2019 International Conference on Multimodal Interaction (2019), 329–338.
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Chunyong Li, Jiguo Xue, Cheng Quan, Jingwei Yue, and Chenggang Zhang. 2018. Biometric recognition via texture features of eye movement trajectories in a visual searching task. PLoS ONE 13, 4 (2018), e0194475.
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          cover image ACM Conferences
          ETRA '21 Short Papers: ACM Symposium on Eye Tracking Research and Applications
          May 2021
          232 pages
          ISBN:9781450383455
          DOI:10.1145/3448018
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          Published: 25 May 2021

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          Author Tags

          1. behavioral features
          2. eye movement
          3. pattern lock
          4. personal identification

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