Abstract
With the development of intelligent electronic devices, users pay more attention to personal information privacy. Among human biometrics, gait does not require user cooperation and is difficult to imitate, making it suitable for implementing highly secure identity authentication. In this work, we demonstrate the application of a multimodal sensing insole for person authentication. For dataset preparation, fixed-length and gait-cycle segmentation were applied. We used a deep learning method to classify the legal user and imposters. The data from twenty subjects were used to train and test models. An average accuracy of more than 99% was achieved. Results confirm the feasibility and effectiveness of using the sensing insole for gait identity authentication.
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References
Liu, J., Sun, W.: Smart attacks against intelligent wearables in people-centric internet of things. IEEE Commun. Mag. 54, 44–49 (2016)
Ahmad, M., Khan, A.M., Brown, J.A., Protasov, S., Khattak, A.M.: Gait fingerprinting-based user identification on smartphones. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3060–3067 (2016)
Papavasileiou, I., Qiao, Z., Zhang, C., Zhang, W., Bi, J., Han, S.: GaitCode: gait-based continuous authentication using multimodal learning and wearable sensors. Smart Health. 19, 100162 (2021)
He, L., Ma, C., Tu, C., Zhang, Y.: Gait2Vec: continuous authentication of smartphone users based on gait behavior. In: 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 280–285 (2022)
Cola, G., Vecchio, A., Avvenuti, M.: Continuous authentication through gait analysis on a wrist-worn device. Pervasive Mobile Comput. 78, 101483 (2021)
Ivanov, K., et al.: Identity recognition by walking outdoors using multimodal sensor insoles. IEEE Access 8, 150797–150807 (2020)
Ivanov, K., et al.: Design of a sensor insole for gait analysis. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds.) ICIRA 2019. LNCS (LNAI), vol. 11743, pp. 433–444. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27538-9_37
Liu, R., Duan, Z., Zhou, J., Liu, M.: Identification of individual walking patterns using gait acceleration. In: 2007 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 543–546 (2007)
Derawi, M.O., Bours, P., Holien, K.: Improved cycle detection for accelerometer based gait authentication. In: 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 312–317 (2010)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Acknowledgments
This work was partially supported by Key R&D support projects of the Chengdu Science and Technology Bureau (No.2021-YF05-02175-SN) and by the funding of the China Scholarship Council.
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Zeng, H. et al. (2022). Identity Authentication Using a Multimodal Sensing Insole—A Feasibility Study. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_50
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DOI: https://doi.org/10.1007/978-3-031-20233-9_50
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