skip to main content
10.1145/2702123.2702518acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
note

Bodyprint: Biometric User Identification on Mobile Devices Using the Capacitive Touchscreen to Scan Body Parts

Published:18 April 2015Publication History

ABSTRACT

Recent mobile phones integrate fingerprint scanners to authenticate users biometrically and replace passwords, making authentication more convenient for users. However, due to their cost, capacitive fingerprint scanners have been limited to top-of-the-line phones, a result of the required resolution and quality of the sensor. We present Bodyprint, a biometric authentication system that detects users' biometric features using the same type of capacitive sensing, but uses the touchscreen as the image sensor instead. While the input resolution of a touchscreen is ~6 dpi, the surface area is larger, allowing the touch sensor to scan users' body parts, such as ears, fingers, fists, and palms by pressing them against the display. Bodyprint compensates for the low input resolution with an increased false rejection rate, but does not compromise on authentication precision: In our evaluation with 12 participants, Bodyprint classified body parts with 99.98% accuracy and identifies users with 99.52% accuracy with a retry likelihood of 26.82% to prevent false positives, thereby bringing reliable biometric user authentication to a vast number of commodity devices.

References

  1. Abate, A.F., Nappi, M., Riccio, D., Sabatino, G. 2D and 3D face recognition. Pattern Recognition Letters (28):14, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. Speededup Robust Features. Proc. CVIU '07, 346--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ben-Asher, N., Kirschnick, N., Sieger, H., Meyer, J., Ben-Oved, A., Möller, S. On the need for different security methods on mobile phones. Proc. MobileHCI '11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Biddle, R., Chiasson, S., Van Oorschot, P. Graphical passwords: Learning from the first twelve years. ACM Computing Surveys, (44)4, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bo, C., Zhang, L., Li, X., Huang, Q., Wang, Y. SilentSense: silent user identification via touch and movement behavioral biometrics. Proc. MobiCom '13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Burge, M. and Burger, W. Ear biometrics. Biometrics 273--285, Springer US, 1996.Google ScholarGoogle Scholar
  7. Cho, D., Park, K.R., Rhee, D.W., Kim, Y., Yang, J. Pupil and Iris Localization for Iris Recognition in Mobile Phones. Proc. SNPD '06, 197--201. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Everitt, K., Bragin, T., Fogarty, J., Kohno, T. A comprehensive study of frequency, interference, and training of multiple graphical passwords. Proc. CHI '09. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Harrison, C., Sato, M., Poupyrev, I. Capacitive Fingerprinting: Exploring User Differentiation by Sensing Electrical Properties of the Human Body. Proc. UIST '12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Holz, C. and Baudisch, P. Fiberio: A Touchscreen that Senses Fingerprints. Proc. UIST '13, 41--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Holz, C. and Baudisch, P. The Generalized Perceived Input Point Model and How to Double Touch Accuracy by Extracting Fingerprints. Proc. CHI '10, 581--590. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kirschnick, N., Kratz, S. and Moller, S. An improved approach to gesture-based authentication for mobile devices. Proc. SOUPS '10.Google ScholarGoogle Scholar
  13. Kurkovsky, S. and Syta, E. Digital natives and mobile phones: A survey of practices and attitudes about privacy and security. Proc. ISTAS '10, 441--449.Google ScholarGoogle Scholar
  14. Richter, S., Holz, C., Baudisch, P. Bootstrapper: Recognizing Tabletop Users by their Shoes. Proc CHI '12. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Bodyprint: Biometric User Identification on Mobile Devices Using the Capacitive Touchscreen to Scan Body Parts

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
      April 2015
      4290 pages
      ISBN:9781450331456
      DOI:10.1145/2702123

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 April 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • note

      Acceptance Rates

      CHI '15 Paper Acceptance Rate486of2,120submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader