Recognition of Fingerprint Biometric System Access Control for Car Memory Settings Through Artificial Neural Networks

  • Abdul RafayEmail author
  • Yumnah Hasan
  • Adnan Iqbal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)


Recognition and authentication are important factors for implementation in every computerized system. This particularly plays a significant role in electronic banking and luxurious cars. PIN code or key can be lost or stolen by an imposter. Therefore, the characteristics of humans are the best recognition points to authenticate a user. Artificial Neural Network (ANN) is the only computational network which works as the working of human brain and its neurons function by adopting the features of a human. In this research, we have proposed an algorithm for training of fingerprint biometric system by implementing Artificial Neural Networks for the recognition of finger features of the human. The method includes detection of minutiae values of the ridge termination and bifurcation points. The multilayer feed forward network is the successful network with error back propagation algorithm for pattern recognition through supervised learning. This network is being used in many applications of recognition and control. This architecture is applicable for finger minutiae extraction for recognition of car user and its features through memory settings. This network gives 99% correct classification for recognition of the user.


Artificial Neural Networks Biometric processor Multilayer feed forward network Pattern recognition 


  1. 1.
    Bennett, S.: A history of control engineering, 1930–1955. No. 47. IET (1993)Google Scholar
  2. 2.
    Lin, N., et al.: An overview on study of identification of driver behavior characteristics for automotive control. Math. Probl. Eng. 2314 (2014)Google Scholar
  3. 3.
    Silver, A., Lewis, L.: Automatic identification of a vehicle driver based on driving behavior. U.S. Patent No. 9,201,932 (2015)Google Scholar
  4. 4.
    Sanchez, K.J., et al.: Systems and methods to identify and profile a vehicle operator. U.S. Patent No. 8,738,523 (2014)Google Scholar
  5. 5.
    Unar, M.A.: Ship steering control using feedforward Neural Networksss. Diss. University of Glasgow (1999)Google Scholar
  6. 6.
    Kunzle, P.: Vehicle control with neural networks. September in Artificial Intelligence (2003)Google Scholar
  7. 7.
    Jain, A., Hong, L., Pankanti, S.: Biometric identification. Commun. ACM 43(2), 90–98 (2000)CrossRefGoogle Scholar
  8. 8.
    Subban, R., Mankame, D.P.: A study of biometric approach using fingerprint recognition. Lect. Notes Softw. Eng. 1(2), 209 (2013)CrossRefGoogle Scholar
  9. 9.
    Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction. Introduction to Biometrics. Springer US, pp. 1–49 (2011)CrossRefGoogle Scholar
  10. 10.
    Abdullah, H.A.: Finger print identification system using neural networks. Nahrain Univ. Coll. Eng. J. (NUCEJ) 15(2), 284–294 (2012)MathSciNetGoogle Scholar
  11. 11.
    Zhili, W.: Fingerprint Recognition. BSc Thesis Hong Kong Baptist University (2002)Google Scholar
  12. 12.
    Sathiaraj, V.: A study on the neural networks model for finger print recognition. Int. J. Comput. Eng. Res. (ijceronline. com) 2 (2012)Google Scholar
  13. 13.
    Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn. 28(11), 1657–1672 (1995)CrossRefGoogle Scholar
  14. 14.
    Barnard, E.: Optimization for training neural nets. IEEE Trans. Neural Netw. 3(2), 232–240 (1992)CrossRefGoogle Scholar
  15. 15.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel Distributed Processing, vol. 1 (1986). In: Rumelhart, D.E., McClelland, J.L. (eds.)Google Scholar
  16. 16.
    Thrun, S.: Finding landmarks for mobile robot navigation. In: 1998 IEEE International Conference on Robotics and Automation, vol. 2. Proceedings. IEEE (1998)Google Scholar
  17. 17.
    Werner, G.A., Hanka, L.: Tuning an artificial Neural Networkss to increase the efficiency of a finger print matching algorithm. IEEE (2016)Google Scholar
  18. 18.
    Bavarian, B.: Introduction to Neural Networksss for intelligent control. IEEE Control Syst. Mag. 8(2), 3–7 (1988)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentBahria UniversityKarachiPakistan

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