Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Visual Computing

ISVC 2018: Advances in Visual Computing pp 167–179Cite as

Authentication-Based on Biomechanics of Finger Movements Captured Using Optical Motion-Capture

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

Abstract

In this paper, we propose an authentication approach based on the uniqueness of the biomechanics of finger movements. We use an optical-marker-based motion-capture as a preliminary setup to capture goniometric (joint-related) and dermatologic (skin-related) features from the flexion and extension of the index and middle fingers of a subject. We use this information to build a personalized authentication model for a given subject. Analysis of our approach using finger motion-capture from 8 subjects, using reflective tracking markers placed around the joints of index and middle fingers of the subjects shows its viability. In this preliminary study, we achieve an average equal error rate (EER)—when false accept rate and false reject rate are equal—of 6.3% in authenticating a subject immediately after training the authentication model and 16.4% ERR after a week.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Equal error rate is the value where the false accept and false reject rates for a model are equal.

  2. 2.

    We use only the first session’s data for training because that’s how typical authentication modality works. We enroll (in our case train the model) once and then subsequently authenticate repeatedly.

References

  1. Opitrack. http://optitrack.com/

  2. The Braille Literacy Crisis in America. https://nfb.org/images/nfb/documents/pdf/braille_literacy_report_web.pdf

  3. Behera, S.K., Kumar, P., Dogra, D.P., Roy, P.P.: Fast signature spotting in continuous air writing. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 314–317. IEEE (2017)

    Google Scholar 

  4. Choraś, M., Kozik, R.: Contactless palmprint and knuckle biometrics for mobile devices. Pattern Anal. Appl. 15(1), 73–85 (2012)

    Article  MathSciNet  Google Scholar 

  5. Gamage, N., Kuang, Y.C., Akmeliawati, R., Demidenko, S.: Gaussian process dynamical models for hand gesture interpretation in sign language. Pattern Recogn. Lett. 32(15), 2009–2014 (2011)

    Article  Google Scholar 

  6. Gupta, P., Gupta, P.: Multi-biometric authentication system using slap fingerprints, palm dorsal vein and hand geometry. IEEE Trans. Ind. Electron., 1 (2018)

    Google Scholar 

  7. Lee, T.: Biometrics and disability rights: legal compliance in biometric identification programs. J. Law Technol. Policy 2016(2), 209–244 (2016)

    Google Scholar 

  8. Marasco, E., Ross, A.: A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput. Surv. (CSUR) 47(2), 28 (2015)

    Google Scholar 

  9. Tagkalakis, F., Vlachakis, D., Megalooikonomou, V., Skodras, A.: A novel approach to finger vein authentication. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 659–662 (2017)

    Google Scholar 

  10. Teh, P.S., Teoh, A.B.J., Yue, S.: A survey of keystroke dynamics biometrics. Sci. World J. 2013 (2013)

    Google Scholar 

  11. Vogiannou, A., Moustakas, K., Tzovaras, D., Strintzis, M.G.: A first approach to contact-based biometrics for user authentication. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 838–846. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01793-3_85

    Chapter  Google Scholar 

  12. Wobbrock, J.O., Kane, S.K., Gajos, K.Z., Harada, S., Froehlich, J.: Ability-based design: concept, principles and examples. ACM Trans. Accessible Comput. (TACCESS) 3(3), 9 (2011)

    Google Scholar 

  13. Wu, J., Christianson, J., Konrad, J., Ishwar, P.: Leveraging shape and depth in user authentication from in-air hand gestures. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3195–3199. IEEE (2015)

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Tess Meier who helped with the data collection for this work. This work is supported by the defense health program grant DHP W81XWH-15-C-0030.

Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Brittany Lewis, Christopher J. Nycz, Gregory S. Fischer & Krishna K. Venkatasubramanian

Authors
  1. Brittany Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher J. Nycz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gregory S. Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krishna K. Venkatasubramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krishna K. Venkatasubramanian .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lewis, B., Nycz, C.J., Fischer, G.S., Venkatasubramanian, K.K. (2018). Authentication-Based on Biomechanics of Finger Movements Captured Using Optical Motion-Capture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03801-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation