Skip to main content
SpringerLink
Log in

Application of Elastic Principal Component Analysis to Person Recognition Based on Screen Gestures

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11508))

Included in the following conference series:

Abstract

Person identification based on touch screen gestures is a well-known method of authentication in mobile devices. Usually it is only checked if the user entered the correct pattern. Taking into account other biometric data based on the speed and shape of finger movements can provide higher security while the convenience of this authorisation method is not impacted. In this work the application of Sequential Joint Functional Principal Analysis (FPCA) as a dimensionality reduction method for gesture data is explored. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of gestures collected from 12 people. The effects of sampling rate on classification performance is also measured. It is shown that the Support Vector Machine classifier reaches the accuracy of 79% using features obtained using the Sequential Joint FPCA, compared to 70% in the case of Euclidean PCA.

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

Access this chapter

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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

Institutional subscriptions

References

  1. Baran, M.: Closest paths in graph drawings under an elastic metric. Int. J. Appl. Math. Comput. Sci. 28(2), 387–397 (2018). https://doi.org/10.2478/amcs-2018-0029

    Article  MathSciNet  MATH  Google Scholar 

  2. Baran, M., Tabor, Z.: Principal geodesic analysis boundary delineation with superpixel-based constraints. Image Anal. Stereology 36(3), 223–232 (2017). https://doi.org/10.5566/ias.1712

    Article  MathSciNet  MATH  Google Scholar 

  3. Bezanson, J., Edelman, A., Karpinski, S., Shah, V.: Julia: a fresh approach to numerical computing. SIAM Review 59(1), 65–98 (2017). https://doi.org/10.1137/141000671

    Article  MathSciNet  MATH  Google Scholar 

  4. Blachnik, M., Głomb, P.: Do we need complex models for gestures? a comparison of data representation and preprocessing methods for hand gesture recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS (LNAI), vol. 7267, pp. 477–485. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29347-4_55

    Chapter  Google Scholar 

  5. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  6. Cheng, W., Dryden, I.L., Huang, X.: Bayesian registration of functions and curves. Bayesian Anal. 11(2), 447–475 (2016). https://doi.org/10.1214/15-BA957

    Article  MathSciNet  MATH  Google Scholar 

  7. Cholewa, M., Głomb, P.: Estimation of the number of states for gesture recognition with hidden Markov models based on the number of critical points in time sequence. Pattern Recogn. Lett. 34(5), 574–579 (2013). https://doi.org/10.1016/j.patrec.2012.12.002

    Article  Google Scholar 

  8. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  9. Fletcher, P., Lu, C., Pizer, S., Joshi, S.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans. Med. Imaging 23(8), 995–1005 (2004). https://doi.org/10.1109/TMI.2004.831793

    Article  Google Scholar 

  10. Ghosh, I.: Probabilistic feature selection in machine learning. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10841, pp. 623–632. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91253-0_58

    Chapter  Google Scholar 

  11. Huckemann, S., Ziezold, H.: Principal component analysis for Riemannian manifolds, with an application to triangular shape spaces. Adv. Appl. Probab. 38(2), 299–319 (2006). https://doi.org/10.1239/aap/1151337073

    Article  MathSciNet  MATH  Google Scholar 

  12. Jain, A., Hong, L., Pankanti, S.: Biometric identification. Commun. ACM 43(2), 90–98 (2000). https://doi.org/10.1145/328236.328110

    Article  Google Scholar 

  13. Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective, 1st edn. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  14. Joshi, S.H., Klassen, E., Srivastava, A., Jermyn, I.: A novel representation for Riemannian analysis of elastic curves in Rn. In: Proceedings/CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–7. IEEE, Minneapolis, July 2007. https://doi.org/10.1109/CVPR.2007.383185

  15. Mani, M., Kurtek, S., Barillot, C., Srivastava, A.: A comprehensive Riemannian framework for the analysis of white matter fiber tracts. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1101–1104, April 2010. https://doi.org/10.1109/ISBI.2010.5490185

  16. Michor, P.W., Mumford, D.B.: Riemannian geometries on spaces of plane curves. J. Eur. Math. Soc. 8(1), 1–48 (2006). https://doi.org/10.4171/JEMS/37

    Article  MathSciNet  MATH  Google Scholar 

  17. Mio, W., Srivastava, A., Joshi, S.: On shape of plane elastic curves. Int. J. Comput. Vis. 73(3), 307–324 (2007). https://doi.org/10.1007/s11263-006-9968-0

    Article  Google Scholar 

  18. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. Adaptive Computation and Machine Learning, 1st edn. The MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  19. Pławiak, P., Sośnicki, T., Niedźwiecki, M., Tabor, Z., Rzecki, K.: Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms. IEEE Trans. Ind. Inform. 12(3), 1104–1113 (2016). https://doi.org/10.1109/TII.2016.2550528

    Article  Google Scholar 

  20. Rzecki, K., Siwik, L., Baran, M.: The elastic \(k\)-nearest neighbours classifier for touch screen gestures, in press

    Google Scholar 

  21. Rzecki, K., Pławiak, P., Niedźwiecki, M., Sośnicki, T., Leśkow, J., Ciesielski, M.: Person recognition based on touch screen gestures using computational intelligence methods. Inf. Sci. 415–416, 70–84 (2017). https://doi.org/10.1016/j.ins.2017.05.041

    Article  Google Scholar 

  22. Srivastava, A., Klassen, E.P.: Functional and Shape Data Analysis. SSS. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-4020-2

    Book  MATH  Google Scholar 

  23. Srivastava, A., Turaga, P., Kurtek, S.: On advances in differential-geometric approaches for 2D and 3D shape analyses and activity recognition. Image Vis. Comput. 30(6–7), 398–416 (2012). https://doi.org/10.1016/j.imavis.2012.03.006

    Article  Google Scholar 

  24. Suarez, J., Murphy, R.R.: Hand gesture recognition with depth images: a review. In: 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 411–417, September 2012. https://doi.org/10.1109/ROMAN.2012.6343787

  25. Sundaramoorthi, G., Mennucci, A., Soatto, S., Yezzi, A.: A new geometric metric in the space of curves, and applications to tracking deforming objects by prediction and filtering. SIAM J. Imaging Sci. 4(1), 109–145 (2011). https://doi.org/10.1137/090781139

    Article  MathSciNet  MATH  Google Scholar 

  26. Tucker, J.D., Wu, W., Srivastava, A.: Generative models for functional data using phase and amplitude separation. Comput. Stat. Data Anal. 61, 50–66 (2013). https://doi.org/10.1016/j.csda.2012.12.001

    Article  MathSciNet  MATH  Google Scholar 

  27. Tucker, J.D., Wu, W., Srivastava, A.: Analysis of proteomics data: phase amplitude separation using an extended Fisher-Rao metric. Electron. J. Stat. 8(2), 1724–1733 (2014). https://doi.org/10.1214/14-EJS900B

    Article  MathSciNet  MATH  Google Scholar 

  28. Turaga, P.K., Srivastava, A. (eds.): Riemannian Computing in Computer Vision. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22957-7

    Book  MATH  Google Scholar 

  29. Vidal, R., Ma, Y., Sastry, S.: Generalized principal component analysis (GPCA). IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1945–1959 (2005). https://doi.org/10.1109/TPAMI.2005.244

    Article  Google Scholar 

  30. Younes, L.: Computable elastic distances between shapes. SIAM J. Appl. Math. 58(2), 565–586 (1998). https://doi.org/10.1137/S0036139995287685

    Article  MathSciNet  MATH  Google Scholar 

  31. Younes, L.: Spaces and manifolds of shapes in computer vision: an overview. Image Vis. Comput. 30(6–7), 389–397 (2012). https://doi.org/10.1016/j.imavis.2011.09.009

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155, Kraków, Poland

    Mateusz Baran & Krzysztof Rzecki

  2. Department of Computer Science, AGH University of Science and Technology, Kraków, Poland

    Leszek Siwik

Authors
  1. Mateusz Baran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leszek Siwik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krzysztof Rzecki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateusz Baran .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Baran, M., Siwik, L., Rzecki, K. (2019). Application of Elastic Principal Component Analysis to Person Recognition Based on Screen Gestures. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20912-4_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation