Machine learning based soft biometrics for enhanced keystroke recognition system

  • T. RamuEmail author
  • K. Suthendran
  • T. Arivoli


The proposed work investigates the performance enhancement of keystroke biometric recognition using soft biometric with filter and Score Boost Weighting (SBW) scheme. Usually, Keystroke recognition performance is lower due to user’s emotional behaviour or distraction, typing patterns vary from user normal position which causes recognition error of genuine user for degrading the recognition accuracy. To address this problem, this work presents Dual Matcher with fusion to reduce the false rejection of genuine user to improve the accuracy of keystroke recognition. In this paper, soft biometric is used as secondary information to improve the recognition accuracy for primary keystroke biometric system. Specifically, soft biometrics provides additional support for keystroke biometric recognition at the combination approach. The performance of keystroke system can be further improved using SVM as machine learning under the score level fusion in the combination approach. Lastly, the fusion technique is used to combine the primary and secondary biometric. The new approach with score fusion enhances the overall performance of keystroke biometric system with 99% accuracy. Maximum of 2% improvement is achieved compared to existing works.


Machine learning Fusion Gaussian probability density function Keystroke biometrics Soft biometrics Support vector machine 



  1. 1.
    Ahilan A, Deepa P (2015) Design for built-in FPGA reliability via fine-grained 2-D error correction codes. Microelectron Reliab 55(9–10):2108–2112CrossRefGoogle Scholar
  2. 2.
    Ahilan A, Deepa P (2016) Improving lifetime of memory devices using evolutionary computing based error correction coding Computational Intelligence, Cyber Security and Computational Models, p 237–245Google Scholar
  3. 3.
    Ahilan A, James EAK (2011) Design and implementation of real time car theft detection in FPGA, 2011 Third International Conference on Advanced Computing, Chennai, p 353–358Google Scholar
  4. 4.
    Ailisto H, Vildjiounaite E, Lindholm M, Mkel SM, Peltola J (2006) Soft biometrics - combining body weight and fat measurements with finger-print biometrics. Pattern Recogn Lett 27(5):325–334CrossRefGoogle Scholar
  5. 5.
    Appathurai A, Manogaran G, Chilamkurti N (2018) Trusted FPGA based transport traffic inject, impersonate (i2) attacks beaconing in internet of vehicles. IET Networks.
  6. 6.
    Bhardwaj I, Londhe ND, Kopparapu SK (2017) A novel behavioural biometric technique for robust user authentication. IETE Tech Rev 34(5):478–490CrossRefGoogle Scholar
  7. 7.
    Bixler R, D’Mello S (2013) Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits. Proceedings of the 2013 international conference on intelligent user interfaces, ACM, p 225–234Google Scholar
  8. 8.
    Bours P (2012) Continuous keystroke dynamics, a different perspective towards biometric evaluation. Inf Secur Tech Rep 17:36–43CrossRefGoogle Scholar
  9. 9.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  10. 10.
    Denman S, Bialkowski A, Fookes C, Sridharan S (2011) Determining operational measures from multi-camera surveillance systems using soft biometrics, 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, p 462–467Google Scholar
  11. 11.
    Dong Y, Woodard DL (2011) Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study. International Joint Conference on Biometrics (IJCB), IEEEGoogle Scholar
  12. 12.
    Epp C, Lippold M, Mandryk R (2011) Identifying emotional states using keystroke dynamics. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, p 715–724Google Scholar
  13. 13.
    Giot R, Rosenberger C (2012) A new soft biometric approach for keystroke dynamics based on gender recognition. International Journal of Information Technology and Management (IJITM) Special Issue on: Advances and Trends in Biometrics by Dr Lidong Wang 11(1/2):35–49Google Scholar
  14. 14.
    Giot R, El-Abed M, Rosenberger C (2011) Keystroke dynamics overview. In: Yang J (ed) Biometrics / Book 1, p 157–182. URL,
  15. 15.
    Heckathorn DD, Broadhead RS, Sergeyev B (1997) A methodology for reducing respondent duplication and impersonation in samples of hidden populations. Annual Meeting of the American Sociological Association, Toronto, CanadaGoogle Scholar
  16. 16.
    Jain AK, Bolle R, Pankanti S (1999) Biometrics: personal identification in networked security. Springer Science & Business MediaGoogle Scholar
  17. 17.
    Jain AK, Dass SC, Nandakumar K (2004) Soft biometric traits for personal recognition systems. In: Proceedings of International Conference on Biometric Authentication, Springer, p 731–738Google Scholar
  18. 18.
    Ren-He J, Wen-Shung C (2016) Two Feature-Level Fusion Methods with Feature Scaling and Hashing for Multimodal Biometrics, IETE Tech Rev 34(1):91–101.
  19. 19.
    John M, Francis K, Richard S, Ralph W (1999) Performance measures for information extraction. Proceedings of DARPA Broadcast News Workshop, Herndon, VAGoogle Scholar
  20. 20.
    Kołakowska A (2018) Usefulness of keystroke dynamics features in user authentication and emotion recognition. In: Hippe Z, Kulikowski J, Mroczek T (eds) Human-computer systems interaction. Advances in intelligent systems and computing, vol 551. Springer, ChamGoogle Scholar
  21. 21.
    Guo GD, Mu G, Ricanek K (2010) Cross-age face recognition on a very large database: the performance versus age intervals and improvement using soft biometric traits. 20th international Conference on Pattern Recognition, p 3392–3395Google Scholar
  22. 22.
    Marcialis GL, Roli F, Muntoni D (2009) Group-specific face verification using soft biometrics. J Vis Lang Comput 20(2):101–109CrossRefGoogle Scholar
  23. 23.
    Morales A, Fierrez J, Tolosana R, Ortega-Garcia J, Galbally J et al (2016) Keystroke biometric ongoing competition, IEEE Access, In Press, p 1–11Google Scholar
  24. 24.
    Obaidat MS, Sadoun B (1999) Keystroke dynamics based authentication. Biometrics. In Biometrics: Personal Identification in Networked Society by Jain, A.K., Bolle, R., & Pankanti, S. (New York: Springer), 213–230Google Scholar
  25. 25.
    Park U, Jain A (2010) Face matching and retrieval using soft biometrics. IEEE Transactions on Information Forensics and Security 5(3):406–415.
  26. 26.
    Prathiba G, Santhi M, Ahilan A (2018) Design and implementation of reliable flash ADC for microwave applications. Microelectron Reliab 88–90:91–97CrossRefGoogle Scholar
  27. 27.
    Roy S, Roy U, Sinha DD (2017) Efficacy of typing pattern analysis in identifying soft biometric information and its impact in user recognition. In: International Conference on Image Analysis and Processing, Springer, Cham, p 320–330Google Scholar
  28. 28.
    Satheesh Kumar J, Saravana Kumar G, Ahilan A (2018) High performance decoding aware FPGA bit-stream compression using RG codes. Springer Cluster Computing, p 1–5Google Scholar
  29. 29.
    Sivasankari B, Ahilan A, Jothin R, Jasmine Gnana Malar A (2018) Reliable N sleep shuffled phase damping design for ground bouncing noise mitigation. Microelectron Reliab 88–90:1316–1321CrossRefGoogle Scholar
  30. 30.
    Syed Idrus SZ, Cherrier E, Rosenberger C, Bours P (2014) Soft biometrics for keystroke dynamics: profiling individuals while typing passwords. Comput Secur 45:147–155.
  31. 31.
    Teh PS, Yue S, Teoh ABJ (2012) Improving keystroke dynamics authentication system via multiple feature fusion scheme. Proceedings of the 2012 International Conference on Cyber SecurityGoogle Scholar
  32. 32.
    Teh PS, Teoh ABJ, Yue S (2013) A survey of Keystroke Dynamics Biometrics. The Scientific world 2013:24.
  33. 33.
    Thanganayagam R, Thangadurai A (2015) Hybrid model with fusion approach to enhance the efficiency of keystroke dynamics authentication. Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics of the series Smart Innovation, Systems and Technologies, Springer India, vol. 43, p 85–96Google Scholar
  34. 34.
    Vapnik V (2000) The nature of statistical learning theory. Springer, BerlinCrossRefGoogle Scholar
  35. 35.
    Wayman JL (1997) Large-scale civilian biometric systems - issues and feasibility. Proceedings of Card Tech / Secur Tech IDGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringKalasalingam UniversityKrishnankoilIndia
  2. 2.School of ComputingKalasalingam UniversityKrishnankoilIndia
  3. 3.Department of Electronics and Communication EngineeringVickram College of EngineeringEnathiIndia

Personalised recommendations