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Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

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Abstract

The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

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References

  1. Jaswal, G., Nigam, A., Nath, R.: DeepKnuckle: revealing the human identity. Multimed. Tools Appl. 76(18), 18955–18984 (2017)

    Article  Google Scholar 

  2. Adeoye, O.S.: A survey of emerging biometric technologies. Int. J. Comput. Appl. 9(10), 1–5 (2010)

    Google Scholar 

  3. Akhtar, Z., Rattani, A., Hadid, A., Tistarelli, M.: Face recognition under ageing effect: a comparative analysis. In: International Conference on Image Analysis and Processing, pp. 309–318 (2013)

  4. Akhtar, Z., Fumera, G., Marcialis, G.L., Roli, F.: Robustness evaluation of biometric systems under spoof attacks. In: International Conference on Image Analysis and Processing, pp. 159–168 (2011)

  5. Jaswal, G., Kaul, A., Nath, R.: Knuckle print biometrics and fusion schemes-overview, challenges, and solutions. ACM Comput. Surv. 49(2), 34 (2016)

    Article  Google Scholar 

  6. Chaa, M., Boukezzoula, N.-E., Attia, A.: Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems. J. Electron. Imaging 26(1), 13018 (2017)

    Article  Google Scholar 

  7. Attia, A., Mourad, C.: Individual recognition system using deep network based on face regions. Int. J. Appl. Math. Electron. Comput. 6(3), 27–32 (2018)

    Google Scholar 

  8. Attia, A., Moussaoui, A., Chaa, M., Chahir, Y.: Finger-Knuckle-Print recognition system based on Features Level Fusion of real and imaginary images. ICTACT J. Image Video Process. 8(4), (2018)

  9. Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of multibiometrics, vol. 6. Springer, Berlin (2006)

    Google Scholar 

  10. Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, Berlin (2007)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Ravikanth, C., Kumar, A.: Biometric authentication using finger-back surface. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007)

  14. Kumar, A.: Can we use minor finger knuckle images to identify humans? In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 55–60 (2012)

  15. Aoyama, S., Ito, K., Aoki, T.: A finger-knuckle-print recognition algorithm using phase-based local block matching. Inf. Sci. (Ny) 268, 53–64 (2014)

    Article  Google Scholar 

  16. Sonawane, S.J., Dhanokar, G.: Verifying Human identities using major and minor finger knuckle pattern-result analysis. Int. J. 1(5), (2016)

  17. Usha, K., Ezhilarasan, M.: Personal recognition using finger knuckle shape oriented features and texture analysis. J. King Saud Univ. Inf. Sci. 28(4), 416–431 (2016)

    Google Scholar 

  18. Kumar, A., Xu, Z.: Personal identification using minor knuckle patterns from palm dorsal surface. IEEE Trans. Inf. Forensics Secur. 11(10), 2338–2348 (2016)

    Article  Google Scholar 

  19. Kusanagi, D., Aoyama, S., Ito, K., Aoki, T.: A practical person authentication system using second minor finger knuckles for door security. IPSJ Trans. Comput. Vis. Appl. 9(1), 8 (2017)

    Article  Google Scholar 

  20. Chlaoua, R., Meraoumia, A., Aiadi, K.E., Korichi, M.: Deep learning for finger-knuckle-print identification system based on PCANet and SVM classifier. Evol. Syst. 10(2), 261–272 (2018)

    Article  Google Scholar 

  21. Chalabi, N.E., Attia, A., Bouziane, A.: Multimodal finger dorsal knuckle major and minor print recognition system based on pcanet deep learning. ICTACT J. Image Video Process. 10(3), 2153–2158 (2020)

    Google Scholar 

  22. Kim, J., Oh, K., Oh, B.-S., Lin, Z., Toh, K.-A.: A line feature extraction method for finger-Knuckle-print verification. Cognit. Comput. 11(1), 50–70 (2019)

    Article  Google Scholar 

  23. Qian, J., Yang, J., Tai, Y., Zheng, H.: Exploring deep gradient information for biometric image feature representation. Neurocomputing 213, 162–171 (2016)

    Article  Google Scholar 

  24. Lalithamani, N., Balaji, R., Ramya, M., Sruthi, S., Aiswarya, A.: Finger Knuckle Biometric Authentication using Convolution Neural Network. Int. J. Pure Appl. Math. 117(10), 31–35 (2017)

    Google Scholar 

  25. Zhai, Y. et al.: A novel finger-Knuckle-print recognition based on batch-normalized CNN. In: Chinese Conference on Biometric Recognition, pp. 11–21 (2018)

  26. Joshi, J.C., Nangia, S.A., Tiwari, K., Gupta, K.K.: Finger Knuckleprint based personal authentication using siamese network. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 282–286 (2019)

  27. Thapar, D., Jaswal, G., Nigam, A.: FKIMNet: a finger dorsal image matching network comparing component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching. arXiv:1904.01289 (2019)

  28. Kumar, A.: Importance of being unique from finger dorsal patterns: exploring minor finger knuckle patterns in verifying human identities. IEEE Trans. Inf. Forensics Secur. 9(8), 1288–1298 (2014)

    Article  Google Scholar 

  29. Kumar, A., Zhou, Y.: Human identification using finger images. IEEE Trans. Image Process. 21(4), 2228–2244 (2012)

    Article  MathSciNet  Google Scholar 

  30. Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)

  31. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  32. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

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Authors and Affiliations

  1. LMSE Laboratory, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria

    Abdelouahab Attia

  2. State University of New York Polytechnic Institute, Marcy, NY, USA

    Zahid Akhtar

  3. University of Caen, Caen, France

    Youssef Chahir

Authors
  1. Abdelouahab Attia
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  2. Zahid Akhtar
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  3. Youssef Chahir
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Correspondence to Abdelouahab Attia.

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Attia, A., Akhtar, Z. & Chahir, Y. Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication. SIViP 15, 851–859 (2021). https://doi.org/10.1007/s11760-020-01806-0

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