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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 226))

Abstract

In this paper an overview of image processing methods for feature extraction applied to knuckle biometrics also termed as FKP (finger-knuckle-print) is presented. Knuckle is a part of hand, and therefore, is easily accessible, invariant to emotions and other behavioral aspects (e.g. tiredness) and most importantly is rich in texture features which usually are very distinctive. In this paper a short overview of the known recent approaches to human identification on the basis of knuckle images is given.

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References

  1. http://webold.iitd.ac.in/~biometrics/knuckle/iitd_knuckle.htm

  2. Gabor, D.: Theory of communication. Journal of the Institute of Electrical Engineers 93, 429–457 (1946)

    Google Scholar 

  3. Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2, 1160–1169 (1985)

    Article  Google Scholar 

  4. Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybernet. 61, 103–113 (1989)

    Article  Google Scholar 

  5. Shiariatmadar, Z.S., Faez, K.: A Novel Approach for Finger-Knuckle Print Recognition Based on Gabor Feature Extraction. In: Proc. of 4th International Congress on Image and Signal Processing, pp. 1480–1484 (2011)

    Google Scholar 

  6. Yang, W., Sun, C., Sun, Z.: Finger-Knuckle Print Recognition Using Gabor Feature and OLDA. In: Proc. of 30th Chinese Control Conference, Yantai, China, pp. 2975–2978 (2011)

    Google Scholar 

  7. 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, Part II. LNCS, vol. 6676, pp. 270–277. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Meraoumia, A., Chitroub, S., Bouridane, A.: Palmprint and Finger Knuckle Print for efficient person recognition based on Log-Gabor filter response. Analog Integr. Circ. Sig. Process. 69, 17–27 (2011)

    Article  Google Scholar 

  9. Cheng, K.Y., Kumar, A.: Contactless Finger Knuckle Identification using Smartphones. In: Proc. of International Conference of the Biometrics Special Interest Group, BIOSIG (2012)

    Google Scholar 

  10. Zhang, L., Zhang, L., Zhang, D.: Finger Knuckle Print: A New Biometric Identifier. In: Proc. of ICIP 2009, pp. 1981–1984. IEEE (2009)

    Google Scholar 

  11. Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Online finger-knuckle-print verification for personal authentication. Pattern Recognition 43, 2560–2571 (2010)

    Article  MATH  Google Scholar 

  12. Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Ensemble of local and global information for finger-knuckle print-recognition. Pattern Recognition 44, 1990–1998 (2011)

    Article  Google Scholar 

  13. Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Information Forensics and Security 4(1), 98–110 (2009)

    Article  Google Scholar 

  14. Choraś, M., Kozik, R.: Knuckle Biometrics Based on Texture Features. In: Proc. of International Workshop on Emerging Techniques and Challenges for Hand-based Biometrics (ETCHB 2010). IEEE CS Press, Stambul (2010)

    Google Scholar 

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis (2004)

    Google Scholar 

  16. Wang, J., Zha, H., Cipolla, R.: Combining interest points and edges for content-based image retrieval. In: Proceedings of the IEEE International Conference on Image Processing (2005)

    Google Scholar 

  17. Morales, A., Travieso, C.M., Ferrer, M.A., Alonso, J.B.: Improved finger-knuckle-print authentication based on orientation enhancement. Electronics Letters 47(6) (2011)

    Google Scholar 

  18. Hemery, B., Giot, R., Rosenberger, C.: Sift Based Recognition of Finger Knuckle Print. In: Proc. of Norwegian Information Security Conference, pp. 45–56 (2010)

    Google Scholar 

  19. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  20. Badrinath, G.S., Nigam, A., Gupta, P.: An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores. In: Qing, S., Susilo, W., Wang, G., Liu, D. (eds.) ICICS 2011. LNCS, vol. 7043, pp. 374–387. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  21. Goh, K.O.M., Tee, C., Teoh, B.J.A.: An innovative contactless palm print and knuckle print recognition system. Pattern Recognition Letters 31, 1708–1719 (2010)

    Article  Google Scholar 

  22. Goh, K.O.M., Tee, C., Teoh, B.J.A.: Bi-modal palm print and knuckle print recognition system. Journal of IT in Asia 3 (2010)

    Google Scholar 

  23. Kumar, A., Zhou, Y.: Human Identification using Knuckle Codes. In: Proc. BTAS (2009)

    Google Scholar 

  24. Zhang, L., Li, H.: Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition. Image and Vision Computing 30, 1043–1051 (2012)

    Article  Google Scholar 

  25. Zhang, L., Zhang, L., Zhang, D.: Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 141–148. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  26. Aoyama, S., Ito, K., Aoki, T.: Finger-Knuckle-Print Recognition Using BLPOC-Based Local block Matching, pp. 525–529. IEEE (2011)

    Google Scholar 

  27. Meraoumia, A., Chitroub, S., Bouridane, A.: Fusion of Finger-Knuckle-Print and Palmprint for an Efficient Multi-biometric System of Person Recognition. In: Proc. of IEEE ICC (2011)

    Google Scholar 

  28. Kumar, A., Prathyusha, K.V.: Personal Authentication Using Hand Vein Triangulation and Knuckle Shape. IEEE Transactions on Image Processing 18(9), 2127–2136 (2009)

    Article  MathSciNet  Google Scholar 

  29. Choraś, M., Kozik, R.: Contactless palmprint and knuckle biometrics for mobile devices. Pattern Analysis and Applications 15(1), 73–85 (2012)

    Article  MathSciNet  Google Scholar 

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Correspondence to Michał Choraś .

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Choraś, M. (2013). A Short Overview of Feature Extractors for Knuckle Biometrics. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_51

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  • DOI: https://doi.org/10.1007/978-3-319-00969-8_51

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00968-1

  • Online ISBN: 978-3-319-00969-8

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