nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Gait Recognition with Multi-region Size Convolutional Neural Network for Authentication with Wearable Sensors

verfasst von : Khac-Tuan Nguyen, Thanh-Luong Vo-Tran, Dat-Thanh Dinh, Minh-Triet Tran

Erschienen in: Future Data and Security Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

As inertial sensors are low-cost, easy-to-use, and can be integrated in wearable devices, they can be used to establish as a new modality for user authentication in the smart environment in which computing systems can recognize persons implicitly by their walking patterns. This motivates our proposal to use multi-region size Convolutional Neural Network to recognize users from their gait patterns recorded from accelerometers and gyroscopes in mobile and wearable devices.

Experiments on Inertial Sensor Dataset of OU-ISIR Gait Database, the largest inertial sensor-based gait database, demonstrate that our best CNN models provide the accuracy of \(96.84\%\) and EER of \(10.43\%\), better than those of existing methods. Furthermore, we also prove by experiments that by using only a subset of subjects in OU-ISIR dataset to train CNN models, our method can achieve the accuracy and EER approximately \((95.53 \pm 0.82)\%\) and \((11.60 \pm 0.98)\%\), respectively.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Mapping Requirements Specifications into a Formalized Blockchain-Enabled Authentication Protocol for Secured Personal Identity Assurance

Nächstes Kapitel Agile Software Engineering Methodology for Information Systems’ Integration Projects

Bianchi, A., Oakley, I.: Wearable authentication: trends and opportunities. Inform. Technol. 58(5), 255–262 (2016)

Choi, S., Youn, I., LeMay, R., Burns, S., Youn, J.: Biometric gait recognition based on wireless acceleration sensor using k-nearest neighbor classification. In: International Conference on Computing, Networking and Communications, ICNC 2014, Honolulu, HI, USA, 3–6 February 2014, pp. 1091–1095 (2014)

Clark, R.A., Pua, Y.H., Bryant, A.L., Hunt, M.A.: Validity of the microsoft kinect for providing lateral trunk lean feedback during gait retraining. Gait Posture 38(4), 1064–1066 (2013)CrossRef

Derawi, M.O., Bours, P.: Gait and activity recognition using commercial phones. Comput. Secur. 39, 137–144 (2013)CrossRef

Derawi, M.O., Bours, P., Holien, K.: Improved cycle detection for accelerometer based gait authentication. In: Echizen, I., Pan, J.S., Fellner, D.W., Nouak, A., Kuijper, A., Jain, L.C. (eds.) IIH-MSP, pp. 312–317. IEEE Computer Society (2010)

Di Stasi, S.L., Logerstedt, D., Gardinier, E.S., Snyder-Mackler, L.: Gait patterns differ between acl-reconstructed athletes who pass return-to-sport criteria and those who fail. Am. J. Sports Med. 41(6), 1310–1318 (2013)CrossRef

Gadaleta, M., Rossi, M.: Idnet: smartphone-based gait recognition with convolutional neural networks. CoRR abs/1606.03238 (2016)

Gafurov, D., Snekkenes, E., Bours, P.: Improved gait recognition performance using cycle matching. In: 24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010, Perth, Australia, 20–13 April 2010, pp. 836–841 (2010)

Hannink, J., Kautz, T., Pasluosta, C., Gassmann, K.G., Klucken, J., Eskofier, B.: Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE J. Biomed. Health Inform. (2016)

10.

Jensen, R.R., Paulsen, R.R., Larsen, R.: Analyzing gait using a time-of-flight camera. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 21–30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02230-2_3 CrossRef

11.

Kirtley, C.: Clinical gait analysis: theory and practice. Elsevier Health Sciences (2006)

12.

Lane, N.D., Georgiev, P.: Can deep learning revolutionize mobile sensing? In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, pp. 117–122. ACM (2015)

13.

Le, C., Jain, R.: A survey of biometrics security systems. EEUU. Washington University in St, Louis (2009)

14.

Lee, J., Kim, D., Ryoo, H.Y., Shin, B.S.: Sustainable wearables: wearable technology for enhancing the quality of human life. Sustainability 8(5), 466 (2016)CrossRef

15.

Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 155–162. IEEE (2002)

16.

Lu, H., Huang, J., Saha, T., Nachman, L.: Unobtrusive gait verification for mobile phones. In: Dunne, L.E., Martin, T., Beigl, M. (eds.) ISWC, pp. 91–98. ACM (2014)

17.

Luo, J., Tang, J., Tjahjadi, T., Xiao, X.: Robust arbitrary view gait recognition based on parametric 3d human body reconstruction and virtual posture synthesis. Pattern Recogn. 60, 361–377 (2016)CrossRef

18.

Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, vol. 30 (2013)

19.

Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S., Ailisto, H.: Identifying users of portable devices from gait pattern with accelerometers. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP 2005), vol. 2, p. ii-973 (2005)

20.

Phan Ba, R., Pierard, S., Moonen, G., van Droogenbroeck, M., Belachew, S.: Detection and quantification of efficiency and quality of gait impairment in multiple sclerosis through foot path analysis. Multiple Sclerosis J. 18, 110 (2012)

21.

Ren, Y., Chen, Y., Chuah, M.C., Yang, J.: User verification leveraging gait recognition for smartphone enabled mobile healthcare systems. IEEE Trans. Mob. Comput. 14(9), 1961–1974 (2015)CrossRef

22.

Rong, L., Jianzhong, Z., Ming, L., Xiangfeng, H.: A wearable acceleration sensor system for gait recognition. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2654–2659, May 2007

23.

Sprager, S., Juric, M.B.: Inertial sensor-based gait recognition: a review. Sensors 15(9), 22089–22127 (2015)CrossRef

24.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015

25.

Tang, Y.: Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013)

26.

Terada, S., Enomoto, Y., Hanawa, D., Oguchi, K.: Performance of gait authentication using an acceleration sensor. In: 2011 34th International Conference on Telecommunications and Signal Processing (TSP), pp. 34–36. IEEE (2011)

27.

Thanh, T.N., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recogn. 47(1), 228–237 (2014)CrossRef

28.

Watanabe, Y.: Influence of holding smart phone for acceleration-based gait authentication. In: EST, pp. 30–33. IEEE Computer Society (2014)

29.

Xue, Z., Ming, D., Song, W., Wan, B., Jin, S.: Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recogn. 43(8), 2904–2910 (2010)CrossRefMATH

30.

Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., Wu, Z.: Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Trans. Cybern. 45(9), 1864–1875 (2015)CrossRef

Titel: Gait Recognition with Multi-region Size Convolutional Neural Network for Authentication with Wearable Sensors
verfasst von: Khac-Tuan Nguyen
Thanh-Luong Vo-Tran
Dat-Thanh Dinh
Minh-Triet Tran
Verlag: Springer International Publishing
Buch: Future Data and Security Engineering
Print ISBN: 978-3-319-70003-8

Electronic ISBN: 978-3-319-70004-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70004-5_14

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner