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Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length

Published: 23 April 2020 Publication History

Abstract

Recent results suggest that biometric identification based on human's eye movement characteristics can be used for authentication. In this paper, we present three new methods and benchmark them against the state-of-the-art. The best of our new methods improves the state-of-the-art performance by 5.2 percentage points. Furthermore, we investigate some of the factors that affect the robustness of the recognition rate of different classifiers on gaze trajectories, such as the type of stimulus and the tracking trajectory length. We find that the state-of-the-art method only works well when using the same stimulus for testing that was used for training. By contrast, our novel method more than doubles the identification accuracy for these transfer cases. Furthermore, we find that with only 90 seconds of eye tracking data, 86.7% accuracy can be achieved.

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References

[1]
Leo Breiman. 1996. Bagging Predictors. Machine Learning 24, 2 (1996), 123--140.
[2]
Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5--32.
[3]
David S. Broomhead and David Lowe. 1988. Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 2, 3 (1988).
[4]
Ali Darwish and Michel Pasquier. 2013. Biometric identification using the dynamic features of the eyes. In IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, BTAS 2013, Arlington, VA, USA, Sept. 29 - Oct. 2, 2013. 1--6.
[5]
Ali Alhaj Darwish. 2013. Biometric Identification Based on Eye Movements and Iris Features Using Task-Driven and Task-Independent Stimuli. Master's thesis. American University of Sharjah.
[6]
Nastaran Maus Esfahani. 2016. A Brief Review of Human Identification Using Eye Movement. Journal of Pattern Recognition Research 11, 1 (2016), 15--24.
[7]
Lee Friedman, Mark S. Nixon, and Oleg V. Komogortsev. 2017. Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases. PLOS ONE 12, 6 (06 2017), 1--42.
[8]
Chiara Galdi and Michele Nappi. 2019. Eye Movement Analysis in Biometrics. Springer Singapore, Singapore, 171--183.
[9]
Chiara Galdi, Michele Nappi, Daniel Riccio, and Harry Wechsler. 2016. Eye movement analysis for human authentication: a critical survey. Pattern Recognition Letters 84 (2016), 272--283.
[10]
Anjith George and Aurobinda Routray. 2016. A score level fusion method for eye movement biometrics. Pattern Recognition Letters 82 (2016), 207--215.
[11]
Tin Kam Ho. 1995. Random decision forests. In Third International Conference on Document Analysis and Recognition, ICDAR 1995, August 14 - 15, 1995, Montreal, Canada. Volume I. 278--282.
[12]
Corey Holland and Oleg V. Komogortsev. 2011. Biometric identification via eye movement scanpaths in reading. In 2011 IEEE International Joint Conference on Biometrics, IJCB 2011, Washington, DC, USA, Oct. 11--13, 2011. 1--8.
[13]
Corey D. Holland and Oleg V. Komogortsev. 2012. Biometric verification via complex eye movements: The effects of environment and stimulus. In IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012, Arlington, VA, USA, September 23--27, 2012. 39--46.
[14]
Tilke Judd, Krista A. Ehinger, Frédo Durand, and Antonio Torralba. 2009. Learning to predict where humans look. In IEEE 12th International Conference on Computer Vision, ICCV 2009, Kyoto, Japan, Sept. 27 - Oct. 4, 2009. 2106--2113.
[15]
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit. 2017. One Model To Learn Them All. arXiv:1706.05137 (2017). http://arxiv.org/abs/1706.05137
[16]
Pawel Kasprowski, Oleg V. Komogortsev, and Alex Karpov. 2012. First eye movement verification and identification competition at BTAS 2012. In IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012, Arlington, VA, USA, September 23--27, 2012. 195--202.
[17]
Pawel Kasprowski and Józef Ober. 2004. Eye Movements in Biometrics. In Biometric Authentication, ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15, 2004, Proceedings. 248--258.
[18]
Tomi Kinnunen and Haizhou Li. 2010. An overview of text-independent speaker recognition: From features to supervectors. Speech Communication 52, 1 (2010), 12--40.
[19]
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, ETRA 2010, Austin, Texas, USA, March 22--24, 2010. 187--190.
[20]
Anneli Olsen and Ricardo Matos. 2012. Identifying parameter values for an I-VT fixation filter suitable for handling data sampled with various sampling frequencies. In Proceedings of the 2012 Symposium on Eye-Tracking Research and Applications, ETRA 2012, Santa Barbara, CA, USA, March 28--30, 2012. 317--320.
[21]
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and others. 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research 12, Oct (2011), 2825--2830.
[22]
Ken Pfeuffer, Matthias J. Geiger, Sarah Prange, Lukas Mecke, Daniel Buschek, and Florian Alt. 2019. Behavioural Biometrics in VR: Identifying People from Body Motion and Relations in Virtual Reality. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, NY, NY, USA, Article 110, 12 pages.
[23]
Ioannis Rigas and Oleg V. Komogortsev. 2017. Current research in eye movement biometrics: An analysis based on BioEye 2015 competition. Image Vision Comput. 58 (2017), 129--141.
[24]
Dario D. Salvucci and Joseph H. Goldberg. 2000. Identifying fixations and saccades in eye-tracking protocols. In Proceedings of the Eye Tracking Research & Application Symposium, ETRA 2000, Palm Beach Gardens, Florida, USA, November 6--8, 2000. 71--78.

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      cover image ACM Conferences
      CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
      April 2020
      10688 pages
      ISBN:9781450367080
      DOI:10.1145/3313831
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 23 April 2020

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      Author Tags

      1. eye movement biometrics
      2. eye tracking
      3. gaze detection

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      • (2024)Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User ExperienceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337203230:5(2257-2268)Online publication date: 1-May-2024
      • (2024)User Identification via Free Roaming Eye Tracking DataHCI International 2024 Posters10.1007/978-3-031-62110-9_39(352-364)Online publication date: 1-Jun-2024
      • (2023)Using Gaze for Behavioural BiometricsSensors10.3390/s2303126223:3(1262)Online publication date: 22-Jan-2023
      • (2023)HotFoot: Foot-Based User Identification Using Thermal ImagingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580924(1-13)Online publication date: 19-Apr-2023
      • (2023)Privacy-preserving datasets of eye-tracking samples with applications in XRIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.324704829:5(2774-2784)Online publication date: 1-May-2023
      • (2023)Recent Advancement in 3D Biometrics using Monocular Camera2023 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB57857.2023.10448655(1-14)Online publication date: 25-Sep-2023
      • (2022)For Your Eyes Only: Privacy-preserving eye-tracking datasets2022 Symposium on Eye Tracking Research and Applications10.1145/3517031.3529618(1-6)Online publication date: 8-Jun-2022
      • (2022)Who do you look like? - Gaze-based authentication for workers in VR2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW55335.2022.00223(744-745)Online publication date: Mar-2022
      • (2022)Privacy-Preserving Viewport Prediction using Federated Learning for 360° Live Video Streaming2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP55362.2022.9950044(1-6)Online publication date: 26-Sep-2022
      • (2022)An extensive study of user identification via eye movements across multiple datasetsImage Communication10.1016/j.image.2022.116804108:COnline publication date: 1-Oct-2022
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