Abstract
In this paper we present a comparative test of different approaches to gait recognition by smartphone accelerometer. Our work provides a twofold contribution. The first one is related to the use of low-cost, built-in sensors that nowadays equip most mobile devices. The second one is related to the use of our system in identification mode. Instead of being used to just verify the identity of the device owner, it can also be used for identification among a set of enrolled subjects. Whether the identification is carried out remotely or even if its results are transmitted to a server, the system can also be exploited in a multibiometric setting. Its results can be fused with those from computer-vision based gait recognition, as well as other biometric modalities, to enforce identification for accessing critical locations/services. We obtained the best results by matching complete walk captures (Recognition Rate 0.95), but the implicit limitation is represented by the fixed number of steps in the walks. Therefore we also investigated methods based on first dividing the signal into steps. The best of these achieved a Recognition Rate of 0.88.
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Gafurov, D.: A survey of biometric gait recognition: approaches, security and challenges. In: Annual Norwegian Computer Science Conference, pp. 19–21 (2007)
Lee, T.K., Belkhatir, M., Sanei, S.: A comprehensive review of past and present vision-based techniques for gait recognition. MTAP 72(3), 2833–2869 (2014)
Derawi, M.O., Nickel, C., Bours, P., Busch, C.: Unobtrusive user-authentication on mobile phones using biometric gait recognition. In: 2010 Sixth Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 306–311 (2010)
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 (IIH-MSP), pp. 312–317. IEEE (2010)
Gafurov, D., Helkala, K., Søndrol, T.: Biometric gait authentication using accelerometer sensor. Journal of Computers 1(7), 51–59 (2006)
Juefei-Xu, F., Bhagavatula, C., Jaech, A., Prasad, U., Savvides, M.: Gait-ID on the move: pace independent human identification using cell phone accelerometer dynamics. In: IEEE BTAS 2012, pp. 8–15. IEEE (2012)
Nickel, C., Busch, C., Rangarajan, S., Mobius, M.: Using hidden markov models for accelerometer-based biometric gait recognition. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA), pp. 58–63. IEEE (2011)
Pan, G., Zhang, Y., Wu, Z.: Accelerometer-based gait recognition via voting by signature points. Electronics Letters 45(22), 1116–1118 (2009)
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 Transactions on Cybernetics, November 2014. doi:10.1109/TCYB.2014.2361287, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6963443&tag=1
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De Marsico, M., Mecca, A. (2015). Biometric Walk Recognizer. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_3
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DOI: https://doi.org/10.1007/978-3-319-23222-5_3
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