Pattern Recognition and Image Analysis

, Volume 27, Issue 1, pp 82–93 | Cite as

Personal identification using the rank level fusion of finger-knuckle-prints

  • J. GroverEmail author
  • M. Hanmandlu
Applied Problems


This paper presents the finger-knuckle-print (FKP) recognition system which comprises three functional phases namely: (1) novel technique for the feature extraction based on the structure function, (2) new classifier based on Triangular norms (T-norms), (3) novel techniques for the rank level fusion. The features derived from the structure function capture the variation in the texture of FKP. We have also proposed a classifier based on Frank T-norm which addresses the uncertainty in the intensity levels of image. We have also adapted the Choquet integral for the rank level fusion to improve further the identification accuracy of the individual FKP. The Choquet integral has never been used for the rank level fusion in the literature. The fuzzy densities will be learned using the reinforced hybrid bacterial foraging-particle swarm optimization (BF-PSO). The integral takes care of the overlapping information between the different instances of FKPs. We have also proposed the use of entropy based function for the rank level fusion. The rigorous experimental results of the rank level fusion show the significant improvement in the identification accuracy.


finger-knuckle-print structure function rank level fusion Choquet integral entropy bacterial foraging particle swarm optimization 


  1. 1.
    Lin Zhang, Lei Zhang, D. Zhang, and H. Zhu, “Online finger-knuckle-print verification for personal authentication,” Pattern Recogn. 43 (7), 2560–2571 (2010).CrossRefzbMATHGoogle Scholar
  2. 2.
    Lin Zhang, Lei Zhang, D. Zhang, and H. Zhu, “Ensemble of local and global information for fingerknuckle- print recognition,” Pattern Recogn. 44 (9), 1990–1998 (2011).CrossRefGoogle Scholar
  3. 3.
    Lin Zhang, Lei Zhang, and D. Zhang, “Fingerknuckle- print: A new biometric identifier,” in Proc. 16th IEEE Int. Conf. on Image Processing (ICIP 2009) (Cairo, Nov. 2009), pp. 1981–1984.Google Scholar
  4. 4.
    Lin Zhang, Lei Zhang, and D. Zhang, “Fingerknuckle- print verification based on band-limited phase-only correlation,” in Proc. 13th Int. Conf. on Computer Analysis of Images and Patterns (CAIP 2009) (Münster, Sept. 2009), pp. 141–148.Google Scholar
  5. 5.
    S. C. Dass, K. Nandakumar, and A. K. Jain, “A principled approach to score level fusion in multimodal bio- + = = S 1 1 i Nc health j J Ji j k l metric systems,” in Proc. 5th Int. Conf. AVBPA (New York, July 2005), pp. 1049–1058.Google Scholar
  6. 6.
    L. Zhang, H. Li, and Y. Shen, “A novel Riesz transforms based coding scheme for finger-knuckle-print recognition,” in Proc. Int. Conf. on Hand-Based Biometrics (ICHB 2011) (Hong-Kong, Nov. 2011), pp. 1–6.Google Scholar
  7. 7.
    Lin Zhang, Lei Zhang, and D. Zhang, “Monogenic code: a novel fast feature coding algorithm with applications to finger-knuckle-print recognition,” in Proc. 1st Int. Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB 2010) (Istanbul, Aug. 2010), pp. 1–4.Google Scholar
  8. 8.
    M. Xiong, W. Yang, and C. Sun, “Finger-knuckle-print recognition using LGBP,” in Proc. 8th Int. Conf. on Advances in Neural Networks (Guilin, May 2011), pp. 270–277.Google Scholar
  9. 9.
    Y. Wankou, S. Changyin, and S. Zhongxi, “Fingerknuckle- print recognition using Gabor feature and OLDA,” in Proc. 30th Chinese Control Conf. (CCC 2011) (Yantai, July 2011), pp. 2975–2978.Google Scholar
  10. 10.
    L. Zhu, “Finger knuckle print recognition based on surf algorithm,” in Proc. 8th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD 2011) (Shanghai, July 2011) pp. 1879–1883.Google Scholar
  11. 11.
    A. Morales, C. M. Travieso, M. A. Ferrer, and J. B. Alonso, “Improved finger-knuckle-print authentication based on orientation enhancement,” Electron. Lett. 47 (6), 380–381 (2011).CrossRefGoogle Scholar
  12. 12.
    H. B. Kekre and V. A. Bharadi, “Finger-knuckle-print region of interest segmentation using gradient field orientation and coherence,” in Proc. 3rd Int. Conf. on Emerging Trends in Engineering and Technology (ICET 2010) (Goa, Nov. 2010), pp. 130–133.Google Scholar
  13. 13.
    X. Jing, W. Li, C. Lan, Y. Yao, X. Cheng, and L. Han, “Orthogonal complex locality preserving projections based on image space metric for finger-knuckle-print recognition,” in Proc. Int. Conf. on Hand-Based Biometrics (ICHB) (Hong Kong, Nov. 2011), pp. 13–17.Google Scholar
  14. 14.
    D. L. Woodard and P. J. Flynn, “Finger surface as a biometric identifier,” Comput. Vision Image Understand. 100 (3), 357–384 (2005).CrossRefGoogle Scholar
  15. 15.
    L. Zhang, L. Zhang, D. Zhang, and Z. Guo, “Phase congruency induced local features for finger-knuckleprint recognition,” Pattern Recogn. 45 (7), 2522–2531 (2012).CrossRefGoogle Scholar
  16. 16.
    C. Ravikanth and A. Kumar, “Biometric authentication using finger-back surface,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2007) (Minneapolis, June 2007), pp. 1–6.Google Scholar
  17. 17.
    M. Hanmandlu, D. Choudhury, and S. Dash, “Detection of defects in fabrics using topothesy fractal dimension features,” Signal, Image Video Processing 9 (7), 1521–1530 (2015).CrossRefGoogle Scholar
  18. 18.
    R. A. Boby, M. Hanmandlu, A. Sharma, and M. Bindal, “Extraction of fractal dimension for iris texture,” in Proc. 5th IAPR Int. Conf. on Biometrics (New Delhi, March 2012), pp. 330–335.Google Scholar
  19. 19.
    A. F. Costa, G. H. Mamani, and A. J. M. Traina, “An efficient algorithm for fractal analysis of textures,” in Proc. 25th SIBGRAPI Conf. on Graphics, Patterns, and Images (Ouro Preto, Aug. 2012), pp. 39–46.Google Scholar
  20. 20.
    B. Schweizer and A. Sklar, Probabilistic Metric Spaces, 1st ed. (Dover, North Holland, 1983).zbMATHGoogle Scholar
  21. 21.
    L. A. Zadeh, “Fuzzy sets,” Inf. Control 8 (3), 338–353 (1965).MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    D. Butnariu, E. P. Klement, and S. Zafrary, “On triangular norm based propositional fuzzy logic,” Fuzzy Sets Syst. 69 (3), 241–255 (1995).MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    P. Hajek, L. Godo, and F. Esteva, “A complete manyvalued logic with product conjunction,” Arch. Math. Logic 35 (3), 191–208 (1996).MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    D. Butnariu and E. P. Klement, Triangular Norm-Based Measures and Games with Fuzzy Coalitions, 2nd ed. (Springer, New York, 1993).CrossRefzbMATHGoogle Scholar
  25. 25.
    E. P. Klement and S. Weber, “Generalized measures,” Fuzzy Sets Syst. 40 (2), 375–394 (1991).MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    P. Bonissone, K. Goebel, and W. Yan, “Classifier fusion using triangular norms,” in Proc. 5th Int. Workshop on Multiple Classifier Systems (Cagliari, June 2004), pp. 154–163.Google Scholar
  27. 27.
    B. D. Baets and H. D. Meyer, “The Frank T-norm family in fuzzy similarity measurement,” in Proc. 2nd EUSFLAT Conf. (Leicester, Sept. 2001), pp. 249–252.Google Scholar
  28. 28.
    T. K. Ho, J. J. Hull, and S. N. Srihari, “Decision combination in multiple classifier systems,” IEEE Trans. Pattern Anal. Mach. Intellig. 16 (1), 66–75 (1994).CrossRefGoogle Scholar
  29. 29.
    A. Kumar and S. Shekhar, “Personal identification using multibiometrics rank-level fusion,” IEEE Trans. Syst, Man, Cybernet. Part C: Appl. Rev. 41 (5), 743–752 (2011).CrossRefGoogle Scholar
  30. 30.
    M. Grabisch, “A new algorithm for identifying fuzzy measures and its application to pattern recognition,” in Proc. 4th IEEE Int. Conf. on Fuzzy Systems (Yokohama, March 1995), pp. 145–150.Google Scholar
  31. 31.
    M. Grabisch, T. Murofushi, and M. Sugeno, “Fuzzy integral for classification and feature extraction,” in Fuzzy Measure and Integrals, Theory, and Applications (Physica-Verlag, Heidelberg, 2000), pp. 415–434.Google Scholar
  32. 32.
    K. Xu, Z. Wang, P. A. Heng, and K. S. Leung, “Classification by nonlinear integral projections,” IEEE Trans. Fuzzy Syst. 11 (2), 187–201 (2003).CrossRefGoogle Scholar
  33. 33.
    W. Wang, Z. Wang, and G. J. Klir, “Genetic algorithms for determining fuzzy measures from data,” J. Intellig. Fuzzy Syst. 6 (2), 171–183 (1998).Google Scholar
  34. 34.
    P. Gader, A. Mendez-Vasquez, K. Chamberlin, J. Bolton, and A. Zare, “Multi-sensor and algorithm fusion with the Choquet integral: applications to landmine detection,” in Proc. 2004 IEEE Int. Geoscience and Remote Sensing Symp., IGARSS (Anchorage, Sept. 2004), pp. 1605–1608.Google Scholar
  35. 35.
    A. M. Vazquez, P. Gader, J. M. Keller, and K. Chamberlin, “Minimum classification error training for Choquet integrals with applications to landmine detection,” IEEE Trans. Fuzzy Syst. 16 (1), 225–238 (2008).CrossRefGoogle Scholar
  36. 36.
    C. W. Tsong and P. Gader, “Word level discriminative training for handwritten word recognition,” in Proc. 7th Int. Workshop on Frontiers in Handwritten Recognition (Amsterdam, Sept. 2000), pp. 393–402.Google Scholar
  37. 37.
    P. Gader and J. Keller, “Applications of fuzzy set theory to handwriting recognition,” in Proc. 3rd IEEE Conf. on Fuzzy Systems, IEEE World Congress on Computational Intelligence (Orlando, June 1994), pp. 910–917.Google Scholar
  38. 38.
    P. D. Gader, M. A. Mohamed, and J. M. Keller, “Dynamic-programming-based handwritten word recognition using the Choquet fuzzy integral as the match function,” J. Electron. Imaging 5 (1), 15–24 (1996).CrossRefGoogle Scholar
  39. 39.
    J. Cao, M. Shridhar, and M. Ahmadi, “Fusion of classifiers with fuzzy integrals,” in Proc. 3rd Int. Conf. on Document Analysis and Recognition, ICDAR (Washington, Aug. 1995), pp. 108–111.Google Scholar
  40. 40.
    A. Ben Khalifa and N. Essoukri BenAmara, “Exploration of the Choquet integral for the fusion of biometric modalities,” in Proc. 9th IEEE Int. Multi-Conf. on Systems, Signals, and Devices (Chemnitz, March 2012), pp. 1–6.Google Scholar
  41. 41.
    A. Ben Khalifa and N. Essoukri BenAmara, “Contribution to the fusion of biometric modalities by the Choquet integral,” Int. J. Image, Graph. Signal Processing 4 (10), 1–7 (2012).CrossRefGoogle Scholar
  42. 42.
    T. Murofushi and M. Sugeno, “A theory of fuzzy measures: representations, Choquet integral and null sets,” J. Math. Anal. Appl. 159 (2), 532–549 (1991).MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    M. A. Mohamed and W. Xiao, “Q-measures: An efficient extension of the Sugeno-measure,” IEEE Trans. Fuzzy Syst. 11 (3), 419–426 (2003).CrossRefGoogle Scholar
  44. 44.
    S. Auephanwiriyakul, J. M. Keller, and P. D. Gader, “Generalized Choquet fuzzy integral fusion,” Inf. Fusion 3 (1), 69–85 (2002).CrossRefGoogle Scholar
  45. 45.
    W. M. Korani, H. T. Dorrah, and H. M. Emara, “Bacterial foraging oriented by particle swarm optimization strategy for PID Tuning,” in Proc. IEEE Int. Symp. on Computational Intelligence in Robotics and Automation (CIRA) (Daejeon, Dec. 2009), pp. 445–450.Google Scholar
  46. 46.
    T. Datta and I. S. Misra, “Improved adaptive bacteria foraging algorithm in optimization of antenna array for faster convergence,” Electromagn. Res. C 1, 143–157 (2008).CrossRefGoogle Scholar
  47. 47.
    M. Hanmandlu and A. Das, “Content-based image retrieval by information theoretic measure,” Defense Sci. J. 61 (5), 415–430 (2011).Google Scholar
  48. 48.
    M. Hanmandlu, J. Grover, S. Vasirkala, and H. M. Gupta, “Error level fusion of multimodal biometrics,” J. Pattern Recogn. Res. 3, 278–297 (2011).CrossRefGoogle Scholar
  49. 49.
    J. Grover and M. Hanmandlu, “Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger-knuckle-print based authentication,” Appl. Soft Comput. 31, 1–13 (2015).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentJamia Hamdard UniversityNew DelhiIndia
  2. 2.Electrical Engineering DepartmentIIT DelhiNew DelhiIndia

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