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BodyLock: Human Identity Recogniser App from Walking Activity Data

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12250))

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Abstract

A person’s identity can be recognized based on his/her biometric data such as fingerprints, voice or gait. A person can also be recognized from his/her gait, which requires having sensors capable of detecting changes in speed and direction of movement. Such sensors are readily available on almost every smartphone model. We perform user identity verification using his/her walking activity data captured by smartphone sensors. To support identity verification, we have developed a mobile application for Android-based devices, which has achieved 97% accuracy of identity verification using data from acceleration, gravity and gyroscope sensors of a smartphone and a linear Support Vector Machine (SVM) classifier. The developed unobtrusive human walking analyser provides an additional active layer of protection, which may invoke a stronger authentication measure (mandatory locking) if a threat threshold is exceeded.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Karolis Kašys, Aurimas Dundulis, Mindaugas Vasiljevas & Robertas Damaševičius

  2. Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliūnas

Authors
  1. Karolis Kašys
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  2. Aurimas Dundulis
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  3. Mindaugas Vasiljevas
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  4. Rytis Maskeliūnas
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  5. Robertas Damaševičius
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Corresponding author

Correspondence to Robertas Damaševičius .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Kašys, K., Dundulis, A., Vasiljevas, M., Maskeliūnas, R., Damaševičius, R. (2020). BodyLock: Human Identity Recogniser App from Walking Activity Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58801-4

  • Online ISBN: 978-3-030-58802-1

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