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International Conference on Computational Collective Intelligence

ICCCI 2017: Computational Collective Intelligence pp 161–170Cite as

Computer User Verification Based on Typing Habits and Finger-Knuckle Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10449))

Abstract

The paper presents preliminary research conducted to assess the potential of biometric methods fusion for continuous user verification. In this article a novel computer user identity verification method based on keystroke dynamics and knuckle images analysis is introduced. In the proposed solution the user verification is performed by means of classification. The introduced approach was tested experimentally using a database which comprises of keystroke dynamics data and knuckle images. The results indicate that the introduced methods fusion performs better than the single biometric approaches.

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References

  1. Banerjee, S.P., Woodard, D.L.: Biometric authentication and identification using keystroke dynamics: a survey. J. Pattern Recogn. Res. 7, 116–139 (2012)

    Article  Google Scholar 

  2. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)

    Article  Google Scholar 

  3. Ng, C.-C., Yap, M.H., Costen, N., Li, B.: Automatic wrinkle detection using hybrid hessian filter. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 609–622. Springer, Cham (2015). doi:10.1007/978-3-319-16811-1_40

    Chapter  Google Scholar 

  4. Doroz, R., Porwik, P., Safaverdi, H.: The new multilayer ensemble classifier for verifying users based on keystroke dynamics. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9330, pp. 598–605. Springer, Cham (2015). doi:10.1007/978-3-319-24306-1_58

    Chapter  Google Scholar 

  5. Doroz, R., et al.: A new personal verification technique using finger-knuckle imaging. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9876, pp. 515–524. Springer, Cham (2016). doi:10.1007/978-3-319-45246-3_49

    Chapter  Google Scholar 

  6. Fager, M., Morris, K.: Quantifying the limits of fingerprint variability. Forensic Sci. Int. 254, 87–99 (2015)

    Article  Google Scholar 

  7. Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Using hand knuckle texture for biometric identifications. IEEE Aerosp. Electron. Syst. Mag. 21(6), 23–27 (2006)

    Article  Google Scholar 

  8. Iwahori, Y., Hattori, A., Adachi, Y., Bhuyan, M.K., Woodham, R.J., Kasugai, K.: Automatic detection of polyp using Hessian Filter and HOG features. Procedia Comput. Sci. 60(1), 730–739 (2015)

    Article  Google Scholar 

  9. Kasprowski, P.: The impact of temporal proximity between samples on eye movement biometric identification. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 77–87. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40925-7_8

    Chapter  Google Scholar 

  10. Koprowski, R., Teper, S.J., Weglarz, B., Wylegała, E., Krejca, M., Wróbel, Z.: Fully automatic algorithm for the analysis of vessels in the angiographic image of the eye fundus. Biomed. Eng. Online 11, 35 (2012)

    Article  Google Scholar 

  11. Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Inf. Forensics Secur. 4(1), 98–110 (2009)

    Article  Google Scholar 

  12. Morales, A., Travieso, C.M., Ferrer, M.A., Alonso, J.B.: Improved finger-knuckle-print authentication based on orientation enhancement. Electron. Lett. 47(6), 380–382 (2011)

    Article  Google Scholar 

  13. Porwik, P., Doroz, R.: Self-adaptive biometric classifier working on the reduced dataset. In: Polycarpou, M., Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 377–388. Springer, Cham (2014). doi:10.1007/978-3-319-07617-1_34

    Chapter  Google Scholar 

  14. Porwik, P., Doroz, R., Wrobel, K.: A new signature similarity measure. In: Proceedings of the 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009, pp. 1022–1027 (2009)

    Google Scholar 

  15. Raiyn, J.: A survey of cyber attack detection strategies. Int. J. Secur. Appl. 8(1), 247–256 (2014)

    Google Scholar 

  16. Salem, M.B., Hershkop, S., Stolfo, S.J.: A survey of insider attack detection research. Adv. Inf. Secur. 39, 69–90 (2008)

    Article  Google Scholar 

  17. Usha, K., Ezhilarasan, M.: Finger knuckle biometrics - a review. Comput. Electr. Eng. 45, 249–259 (2015)

    Article  Google Scholar 

  18. Wesołowski, T.E., Porwik, P.: Keystroke data classification for computer user profiling and verification. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS, vol. 9330, pp. 588–597. Springer, Cham (2015). doi:10.1007/978-3-319-24306-1_57

    Chapter  Google Scholar 

  19. Wesołowski, T.E., Porwik, P.: Computer user profiling based on keystroke analysis. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds.) Advanced Computing and Systems for Security. AISC, vol. 395, pp. 3–13. Springer, New Delhi (2016). doi:10.1007/978-81-322-2650-5_1

    Chapter  Google Scholar 

  20. Wesolowski, T.E., Porwik, P., Doroz, R.: Electronic health record security based on ensemble classification of keystroke dynamics. Appl. Artif. Intell. 30, 521–540 (2016)

    Article  Google Scholar 

  21. Woodard, D.L., Flynn, P.J.: Finger surface as a biometric identifier. Comput. Vis. Image Underst. 100(3), 357–384 (2005)

    Article  Google Scholar 

  22. Xiong, M., Yang, W., Sun, C.: Finger-knuckle-print recognition using LGBP. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011. LNCS, vol. 6676, pp. 270–277. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21090-7_32

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia in Katowice, Katowice, Poland

    Hossein Safaverdi, Tomasz Emanuel Wesolowski, Rafal Doroz, Krzysztof Wrobel & Piotr Porwik

Authors
  1. Hossein Safaverdi
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  2. Tomasz Emanuel Wesolowski
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  3. Rafal Doroz
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  4. Krzysztof Wrobel
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  5. Piotr Porwik
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Corresponding authors

Correspondence to Tomasz Emanuel Wesolowski or Krzysztof Wrobel .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Safaverdi, H., Wesolowski, T.E., Doroz, R., Wrobel, K., Porwik, P. (2017). Computer User Verification Based on Typing Habits and Finger-Knuckle Analysis. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-67077-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67076-8

  • Online ISBN: 978-3-319-67077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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