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

  • Brittany Lewis
  • Christopher J. Nycz
  • Gregory S. Fischer
  • Krishna K. VenkatasubramanianEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11241)


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.


Authentication Biometrics Finger biomechanics Motion-capture 



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.


  1. 1.
  2. 2.
  3. 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. 4.
    Choraś, M., Kozik, R.: Contactless palmprint and knuckle biometrics for mobile devices. Pattern Anal. Appl. 15(1), 73–85 (2012)MathSciNetCrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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. 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. 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. 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. 10.
    Teh, P.S., Teoh, A.B.J., Yue, S.: A survey of keystroke dynamics biometrics. Sci. World J. 2013 (2013)Google Scholar
  11. 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). Scholar
  12. 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. 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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Brittany Lewis
    • 1
  • Christopher J. Nycz
    • 1
  • Gregory S. Fischer
    • 1
  • Krishna K. Venkatasubramanian
    • 1
    Email author
  1. 1.Worcester Polytechnic InstituteWorcesterUSA

Personalised recommendations