Abstract
The saccadic trajectory is generated by extra-ocular muscles in the eyes, which is a complex mechanism related to brain-driven neural signal. The saccadic trajectory has the characteristics of non-reproducibility and non-contact. In this paper, we propose a saccadic trajectory-based identity authentication method considering that saccadic trajectory can be used as a behavior-based biometric. In this method, we adopt Velocity-Threshold (I-VT) algorithm to extract saccadic trajectories from the whole eye movement data, extract features via wavelet packet transform and authenticate the identity via classifying these features by SVM. In this paper, we verify the proposed method on EMDBv1.0 dataset for horizontal eye movements. We select one subject to be the host and randomly choose another 50 subjects from the remaining 58 subjects as the attackers. We achieve the best performance via optimizing feature selection and the parameter of SVM. The experiment results show that the average accuracy for accepting the host can reach 98.09%, and the average accuracy for rejecting the attackers can reach 99.55%. It demonstrates that the saccadic trajectory-based identity authentication is promising in information security.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bednarik R, Kinnunen T, Mihaila A, Frńti P (2005) Eye-movements as a biometric. In: Scandinavian conference on image analysis, pp. 780–789
Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180–1197
Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Press, Piscataway
Ding C, Tao D (2015) Robust face recognition via multimodal deep face representation. IEEE Trans Multimed 17(11):2049–2058
Kasprowski P, Ober J (2004) Eye movements in biometrics. In: Biometric authentication, ECCV 2004 international workshop, bioAW 2004. Proceedings, Prague, pp 248–258
Kinnunen T, Sedlak F, Bednarik R (2010) Towards task-independent person authentication using eye movement signals. In: Symposium on Eye-tracking Research & applications, etra 2010, Austin, pp 187–190
Komogortsev OV, Gobert DV, Jayarathna S, Koh DH, Gowda S (2010) Standardization of automated analyses of oculomotor fixation and saccadic behaviors. IEEE Trans Biomed Eng 57(11):2635–2645
Komogortsev OV, Jayarathna S, Aragon C, Mahmoud M (2010) Biometric identification via an oculomotor plant mathematical model. In: Eye Tracking Research & Application, pp 57–60
Komogortsev OV, Karpov A, Price LR, Aragon C (2012) Biometric authentication via oculomotor plant characteristics. In: Iapr International Conference on Biometrics, pp 413–420
Leigh RJ, Zee DS (2015) A Survey of Eye Movements
Li C, Zhou W, Yuan S (2015) Iris recognition based on a novel variation of local binary pattern. Springer, New York
Li Y, Zheng W, Cui Z, Zhang T (2018) Face recognition based on recurrent regression neural network. Neurocomputing
Liang Z, Tan F, Chi Z (2012) Video-based biometric identification using eye tracking technique. In: IEEE International conference on signal processing, communication and computing, pp 728–733
Luo M, Nie F, Chang X, Yang Y, Hauptmann AG, Zheng Q (2018) Adaptive unsupervised feature selection with structure regularization. IEEE Trans Neural Netw Learn Syst PP(99):1–13
Ma Z, Chang X, Yang Y, Sebe N, Hauptmann AG (2017) The many shades of negativity. IEEE Trans Multimed 19(7):1558–1568
Mukhopadhyay S, Nandi S (2017) Lpitrack: Eye movement pattern recognition algorithm and application to biometric identification. Machine Learning 107(2):1–19
Roy K, Bhattacharya P, Debnath RC (2008) Multi-class svm based iris recognition. In: International conference on computer and information technology, pp 1–6
Silver DL, Biggs A (2006) Keystroke and eye-tracking biometrics for user identification. In: International conference on artificial intelligence, icai 2006, Las Vegas, pp 344–348
Wang YB, De-Min YU, Zeng-Pu XU, Wang YQ (2009) Feature extraction and recognition of loudspeaker response signal based on principal components analysis. Journal of Tianjin University of Science & Technology
Wang Z, Li C, Shao H, Sun J (2018) Eye recognition with mixed convolutional and residual network(micore-net). IEEE Access PP(99):1–1
Zhang Y, Juhola M (2012) On biometric verification of a user by means of eye movement data mining, pp 85–90
Acknowledgements
This work is supported by Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201718), Key Research and Development Foundation of Shandong Province (2016GGX101009), the Natural Science Foundation of China (U1736122) and Shandong Provincial. Key Research and Development Plan (2017CXGC1504). The contact author is Wenbo Wan (wanwenbo@sdnu.edu.cn) and Jiande Sun (jiandesun@hotmail.com).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shao, H., Li, J., Wan, W. et al. Saccadic trajectory-based identity authentication. Multimed Tools Appl 79, 4891–4905 (2020). https://doi.org/10.1007/s11042-018-6816-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-6816-5