Skip to main content
SpringerLink
Log in

Palmprint Recognition with Deep Convolutional Features

  • Conference paper
  • First Online:
Book cover Advances in Image and Graphics Technologies (IGTA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 757))

Included in the following conference series:

Abstract

Palmprint recognition has become popular and significant in many fields because of its high efficiency and accuracy in personal identification. In this paper, we present a scheme for palmprint features extraction based on deep convolutional neural network (CNN). The CNN, which naturally integrates low/mid/high-level feature, performs excellently in processing images, video and speech. We extract the palmprint features using the CNN-F architecture, and exactly evaluate the convolutional features from different layers in the network for both identification and verification tasks. The experimental results on public PolyU palmprint database illuminate that palmprint features from the CNN-F respectively achieve the optimal identification rate of 100% and verification accuracy of EER = 0.25%, which demonstrate the effectiveness and reliability of the proposed palmprint CNN features.

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. Zhang, D., Shu, W.: Two novel characteristics in palmprint verification: datum point invariance and line feature matching. Pattern Recogn. 32(4), 691–702 (1999)

    Article  MathSciNet  Google Scholar 

  2. Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recogn. Lett. 23(4), 477–485 (2002)

    Article  MATH  Google Scholar 

  3. Zhang, D., Kong, W., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)

    Article  Google Scholar 

  4. Kong, W.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: International Conference on Pattern Recognition, pp. 23–26 (2004)

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  7. Fei, L.K., Xu, Y., David, Z.: Half-orientation extraction of palmprint features. Pattern Recogn. Lett. 69(C), 35–41 (2016)

    Google Scholar 

  8. Lu, G., Zhang, D., Wang, K.: Palmprint recognition using eigenpalms features. Pattern Recogn. Lett. 24(9–10), 1463–1467 (2003)

    Article  MATH  Google Scholar 

  9. Zhang, S., Lei, Y.K., Wu, Y.H.: Semi-supervised locally discriminant projection for classification and recognition. Knowl.-Based Syst. 24(2), 341–346 (2011)

    Article  Google Scholar 

  10. Yan, Y., Wang, H., Chen, S., et al.: Quadratic projection based feature extraction with its application to biometric recognition. Pattern Recogn. 56(C), 40–49 (2016)

    Google Scholar 

  11. Li, W.X., Zhang, D., Xu, Z.Q.: Palmprint recognition based on Fourier transform. J. Softw. 13(5), 879–886 (2002)

    Google Scholar 

  12. Krishneswari, K., Arumugam, S.: Intramodal feature fusion using wavelet for palmprint authentication. Int. J. Eng. Sci. Technol. 3(2), 1597–1605 (2011)

    Google Scholar 

  13. Prasad, S.M., Govindan, V.K., Sathidevi, P.S.: Palmprint authentication using fusion of wavelet based representations. In: World Congress on Nature & Biologically Inspired Computing, pp. 15–17 (2010)

    Google Scholar 

  14. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Vincent, P., Larochelle, H., Lajoie, I., et al.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(6), 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25(2), 1097–1105 (2012)

    Google Scholar 

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Comput. Sci. (2015)

    Google Scholar 

  19. Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  20. Donahue, J., Jia, Y., Vinyals, O., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. Comput. Sci. 50(1), 815–830 (2013)

    Google Scholar 

  21. Razavian, A.S., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: Computer Vision and Pattern Recognition Workshops, pp. 24–29 (2014)

    Google Scholar 

  22. Chatfield, K., Simonyan, K., Vedaldi, A., et al.: Return of the devil in the details: delving deep into convolutional nets. Comput. Sci. (2014)

    Google Scholar 

  23. The Hong Kong Polytechnic University. PolyU Palmprint Database. (2004,1,1) [2006,7,15]. http://www.comp.polyt.edu.hk/~biometrics/

  24. Meraoumia, A., Chitroub, S., Bouridane, A.: Gaussian modeling and Discrete Cosine Transform for efficient and automatic palmprint identification. In: International Conference on Machine and Web Intelligence, pp. 121–125 (2010)

    Google Scholar 

  25. Saedi, S., Charkari, N.M.: Palmprint authentication based on discrete orthonormal S-Transform. Appl. Softw. Comput. 21(8), 341–351 (2014)

    Article  Google Scholar 

  26. Luo, Y.T., Zhao, L.Y., Zhang, B., et al.: Local line directional pattern for palmprint recognition. Pattern Recog. 50(C), 26–44 (2016)

    Google Scholar 

  27. Hong, D., Liu, W., Wu, X., et al.: Robust palmprint recognition based on the fast variation Vese-Osher model. Neurocomputing 174, 999–1012 (2015)

    Article  Google Scholar 

  28. Butt, M.A.A., Masood, H., Mumtaz, M., et al.: Palmprint identification using contourlet transform. In: International Conference on Biometrics: Theory, Applications and Systems, pp. 1–5 (2008)

    Google Scholar 

  29. Ekinci, M., Aykut, M.: Gabor-based kernel PCA for palmprint recognition. Electron. Lett. 43(20), 1077–1079 (2007)

    Article  Google Scholar 

  30. Wang, X., Lei, L., Wang, M.: Palmprint verification based on 2D-Gabor wavelet and pulse-coupled neural network. Knowl.-Based Syst. 27(3), 451–455 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the support from the National Natural Science Foundation of China (No. 91546123), the Program for Liaoning Innovative Research Team in University (No. LT2015002), the Liaoning Provincial Natural Science Foundation (No. 201602035) and the High-level Talent Innovation Support Program of Dalian City (No. 2016RQ078).

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University Dalian, Dalian, People’s Republic of China

    Qiule Sun, Jianxin Zhang, Aoqi Yang & Qiang Zhang

Authors
  1. Qiule Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianxin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aoqi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Zhang .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Yongtian Wang

  2. Tsinghua University, Beijing, China

    Shengjin Wang

  3. Beijing Institute of Technology, Beijing, China

    Yue Liu

  4. Beijing Institute of Technology, Beijing, China

    Jian Yang

  5. School of EECS, Center for Information Science, Peking University, Beijing, China

    Xiaoru Yuan

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  7. La Trobe University, Melbourne, Victoria, Australia

    Henry Been-Lirn Duh

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, Q., Zhang, J., Yang, A., Zhang, Q. (2018). Palmprint Recognition with Deep Convolutional Features. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7389-2_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7388-5

  • Online ISBN: 978-981-10-7389-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation