Skip to main content
SpringerLink
Log in

A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

  • Conference paper
  • First Online:
Biometric Recognition (CCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10996))

Included in the following conference series:

Abstract

Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fei, L., Wen, J., Zhang, Z., et al.: Local multiple directional pattern of palmprint image, 3013–3018. In: The 23rd International Conference on Pattern Recognition (ICPR) (2016)

    Google Scholar 

  2. Bapat, A., Kanhangad, V.: Segmentation of hand from cluttered backgrounds for hand geometry biometrics. In: IEEE Region 10 Symposium (TENSYMP), pp. 1–4 (2017)

    Google Scholar 

  3. Chatterjee, A., Bhatia, V., Prakash, S.: Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis. Opt. Lasers Eng. 95, 1–7 (2017)

    Article  Google Scholar 

  4. Huang, D., Zhang, R., Yin, Y., et al.: Local feature approach to dorsal hand vein recognition by centroid-based circular key-point grid and fine-grained matching. Image Vis. Comput. 58, 266–277 (2017)

    Article  Google Scholar 

  5. Zhang, L., Zhang, L., Zhang, D., et al.: Online finger-knuckle-print verification for personal authentication. Pattern Recogn. 43(7), 2560–2571 (2010)

    Article  Google Scholar 

  6. Zhang, L., Zhang, L., Zhang, D., et al.: Ensemble of local and global information for finger-knuckle-print recognition. Pattern Recogn. 44(9), 1990–1998 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Woodard, D.L, Flynn, P.J.: Personal identification utilizing finger surface features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1030–1036 (2005)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  12. Zhang, L., Zhang, L., Zhang, D.: Finger-knuckle-print: a new biometric identifier. In: IEEE International Conference on Image Processing (ICIP), pp. 1981–1984 (2009)

    Google Scholar 

  13. 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). https://doi.org/10.1007/978-3-642-03767-2_17

    Chapter  Google Scholar 

  14. Morales, A., Travieso, C.M., Ferrer, M.A., et al.: Improved finger-knuckle-print authentication based on orientation enhancement. Electron. Lett. 47(6), 380–381 (2011)

    Article  Google Scholar 

  15. Le, Z.: Finger knuckle print recognition based on surf algorithm. In: The Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3, pp. 1879–1883 (2011)

    Google Scholar 

  16. 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). https://doi.org/10.1007/978-3-642-25243-3_30

    Chapter  Google Scholar 

  17. Li, Z., Wang, K., Zuo, W.: Finger-knuckle-print recognition using local orientation feature based on steerable filter. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 224–230. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31837-5_33

    Chapter  Google Scholar 

  18. Yang, W., Sun, C., Zhang, L.: A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn. 44(8), 1649–1657 (2011)

    Article  Google Scholar 

  19. Zhang, L., Li, H.: Encoding local image patterns using riesz transforms: with applications to palmprint and finger-knuckle-print recognition. Image Vis. Comput. 30(12), 1043–1051 (2012)

    Article  MathSciNet  Google Scholar 

  20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105(2012)

    Google Scholar 

  21. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448–456 (2015)

    Google Scholar 

  22. Yu, H., Yang, G., Wang, Z., et al.: A new finger-knuckle-print ROI extraction method based on two-stage center point detection. Int. J. Sig. Process. Image Proc. Pattern Recogn. 8(2), 185–200 (2015)

    Google Scholar 

  23. 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(1), 17–27 (2011)

    Article  Google Scholar 

  24. 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. LNCS, vol. 6676, pp. 270–277. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21090-7_32

    Chapter  Google Scholar 

  25. PolyU Finger-Knuckle-Print Database: http://www.comp.polyu.edu.hk/~biometrics (2010)

  26. Kirthiga, R., Ramesh, G.: Efficient FKP based recognition system using k-mean clustering for security system. SSRG Int. J. Electron. Commun. Eng. 1–7 (2016)

    Google Scholar 

  27. Konda, K., Bouthillier, X., Memisevic, R., et al.: Dropout as data augmentation. Computer. Science 1050, 29 (2015)

    Google Scholar 

  28. Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  29. Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)

    Google Scholar 

  30. Leung, H., Haykin, S.: The complex backpropagation algorithm. IEEE Trans. Signal Process. 39(9), 2101–2104 (1991)

    Article  Google Scholar 

  31. Sun, Z., Tan, T., Wang, Y., et al. Ordinal palmprint representation for personal identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 279–284 (2005)

    Google Scholar 

  32. Guo, Z., Zhang, D., Zhang, L., et al.: Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn. Lett. 30(13), 1219–1227 (2009)

    Article  Google Scholar 

  33. Jia, W., Huang, D.S., Zhang, D.: Palmprint verification based on robust line orientation code. Pattern Recogn. 41(5), 1504–1513 (2008)

    Article  Google Scholar 

  34. Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  35. Kong, A.W.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: The 17th International Conference on Pattern Recognition (ICIR), vol. 1, pp. 520–523 (2004)

    Google Scholar 

Download references

Acknowledgments

This work is supported by National of Nature Science Foundation Grant (No. 61372193, No. 61771347), Guangdong Higher Education Outstanding Young Teachers Training Program Grant (No. SYQ2014001), Characteristic Innovation Project of Guangdong Province (No. 2015KTSCX 143, 2015KTSCX145, 2015KTSCX148), Youth Innovation Talent Project of Guangdong Province (No. 2015KQNCX172, No. 2016KQNCX171), Science and Technology Project of Jiangmen City (No. 201501003001556, No. 201601003002191), and China National Oversea Study Scholarship Fund.

Author information

Authors and Affiliations

  1. School of Information Engineering, Wuyi University, Dongchengstr. 22, Jiangmen, 529020, China

    Yikui Zhai, He Cao, Lu Cao, Hui Ma, Junyin Gan, Junying Zeng, Wenbo Deng & Jinxin Wang

  2. School of Computer, Wuyi University, Dongchengstr. 22, Jiangmen, 529020, China

    Yihang Zhi

  3. Dipartimento Di Informatica, Universita’ degli Studi di Milano, Milan, 26013, CR, Italy

    Vincenzo Piuri & Fabio Scotti

Authors
  1. Yikui Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. He Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junyin Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junying Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vincenzo Piuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fabio Scotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenbo Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yihang Zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jinxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Cao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Xinjiang University, Urumqi, China

    Zhenhong Jia

  5. Tsinghua University, Beijing, China

    Jianjiang Feng

  6. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  7. Xinjiang University, Urumqi, China

    Kurban Ubul

  8. Tsinghua University, Shenzhen, China

    Zhenhua Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhai, Y. et al. (2018). A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97909-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97908-3

  • Online ISBN: 978-3-319-97909-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation