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Multitrait Selfie: Low-Cost Multimodal Smartphone User Authentication

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Biometric Identification Technologies Based on Modern Data Mining Methods

Abstract

Biometric identification is biometric-based authentication on mobile devices that nowadays has become ubiquitous, especially in unattended (e.g., access control for banks) and consumer (e.g., mobile phone unlocking) applications. While, face, fingerprint and inherent behavioral biometrics using inbuilt sensors such as accelerometer for smartphones person authentication, they have yet not achieved the desired or required level of efficiency, security and usability. This chapter presents an uncontrolled multibiometric smartphone framework utilizing multitrait selfie and behavioral biometrics. In particular, the presented system authenticates subject by ocular and face selfie features. This new multimodal biometric system also takes silently into account micro-movements of the phone, movements of the user’s finger on the touchscreen while user is capturing the multitrait selfie and entering passcode simultaneously in a split-screen mode of the smartphone. Addition of micro-movements behaviors enhances not only the performance but also robustness against noise and spoofing attacks. For this study, we collected a mobile multimodal dataset (MultiTouchMove) of touchstroke and phone-movement patterns in the wild from 95 subjects, which is made publicly available by the authors. Preliminary experimental analysis, using public MOBIO Face, VISOB ocular, and MultiTouchMove mobile datasets, on accuracy and usability shows promising results.

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Notes

  1. 1.

    Just 2 days after the iPhone5s hit the market, it was fooled by a fingerprint spoof [3].

  2. 2.

    http://abcnews.go.com/blogs/technology/2013/05/cellphone-users-check-phones-150xday-andother-internet-fun-facts/.

  3. 3.

    Touch-typing is the act of typing input on the touchscreen of a smartphone.

  4. 4.

    The database is made publicly available for research purposes and can be obtained by contacting the authors.

  5. 5.

    http://www.galaxyresearch.com.au/.

  6. 6.

    https://www.miteksystems.com/.

  7. 7.

    http://developer.android.com/guide/topics/sensors/sensors.motion.html.

  8. 8.

    These are our rudimentary results. We are presently developing inexpensive deep learning topologies for face recognition in-the-wild on smartphones for elevated accuracy and interoperability.

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Author information

Authors and Affiliations

  1. University of Memphis, Memphis, TN, USA

    Zahid Akhtar

  2. University of Bolzano, Bolzano, Italy

    Attaullah Buriro

Authors
  1. Zahid Akhtar
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  2. Attaullah Buriro
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Corresponding authors

Correspondence to Zahid Akhtar or Attaullah Buriro .

Editor information

Editors and Affiliations

  1. State University of Infrastructure and Technology, Kyiv, Ukraine

    Stepan Bilan

  2. College of Computer Information Technology, American University in the Emirates, Emirates, United Arab Emirates

    Mohamed Elhoseny

  3. Karunya University, Karunya Nagar, India

    D. Jude Hemanth

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Akhtar, Z., Buriro, A. (2021). Multitrait Selfie: Low-Cost Multimodal Smartphone User Authentication. In: Bilan, S., Elhoseny, M., Hemanth, D.J. (eds) Biometric Identification Technologies Based on Modern Data Mining Methods. Springer, Cham. https://doi.org/10.1007/978-3-030-48378-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-48378-4_11

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-48378-4

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