Skip to main content

Cancelable Template Generation Using Convolutional Autoencoder and RandNet

Part of the Communications in Computer and Information Science book series (CCIS,volume 1567)


The security of biometric systems has always been a challenging area of research to safeguard against the day-by-day introduction of new attacks with the advancement in technology. Cancelable biometric templates have proved to be an effective measure against these attacks while ensuring an individual’s privacy. The proposed scheme uses a convolutional autoencoder (CAE) for feature extraction, a rank-based partition network, and a random network to construct secured cancelable biometric templates. Evaluation of the proposed secured template generation scheme has been done on the face and palmprint modalities.


  • Cancelable biometric
  • Convolutional autoencoder
  • RandNet
  • Random permutation flip
  • Secure sketch
  • Seperable convolution

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions


  1. Cao, K., Jain, A.K.: Learning fingerprint reconstruction: from minutiae to image. IEEE Trans. Inf. Forensics Secur. 10(1), 104–117 (2014)

    CrossRef  Google Scholar 

  2. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  3. Durstenfeld, R.: Algorithm 235: random permutation. Commun. ACM 7(7), 420 (1964)

    CrossRef  Google Scholar 

  4. Dusmanu, M., et al.: D2-Net: a trainable CNN for joint description and detection of local features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8092–8101 (2019)

    Google Scholar 

  5. Feng, Y.C., Lim, M.H., Yuen, P.C.: Masquerade attack on transform-based binary-template protection based on perceptron learning. Pattern Recogn. 47(9), 3019–3033 (2014)

    CrossRef  Google Scholar 

  6. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323. JMLR Workshop and Conference Proceedings (2011)

    Google Scholar 

  7. Gomez-Barrero, M., Galbally, J., Rathgeb, C., Busch, C.: General framework to evaluate unlinkability in biometric template protection systems. IEEE Trans. Inf. Forensics Secur. 13(6), 1406–1420 (2018).

    CrossRef  Google Scholar 

  8. Hammad, M., Liu, Y., Wang, K.: Multimodal biometric authentication systems using convolution neural network based on different level fusion of ECG and fingerprint. IEEE Access 7, 26527–26542 (2018)

    CrossRef  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Ide, H., Kurita, T.: Improvement of learning for CNN with ReLU activation by sparse regularization. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, pp. 2684–2691 (2017)

    Google Scholar 

  11. Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Signal Process. 2008, 1–17 (2008)

    CrossRef  Google Scholar 

  12. Jang, Y.K., Cho, N.I.: Deep face image retrieval for cancelable biometric authentication. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2019)

    Google Scholar 

  13. Kirch, W. (ed.): Pearson’s Correlation Coefficient, pp. 1090–1091. Springer, Netherlands, Dordrecht (2008).

  14. Kumar, A.: Incorporating cohort information for reliable palmprint authentication. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 583–590. IEEE (2008)

    Google Scholar 

  15. Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)

    CrossRef  Google Scholar 

  16. Liu, Y., Ling, J., Liu, Z., Shen, J., Gao, C.: Finger vein secure biometric template generation based on deep learning. Soft. Comput. 22(7), 2257–2265 (2017).

    CrossRef  Google Scholar 

  17. Lumini, A., Nanni, L.: An improved biohashing for human authentication. Pattern Recogn. 40(3), 1057–1065 (2007)

    CrossRef  Google Scholar 

  18. Mai, G., Cao, K., Lan, X., Yuen, P.C.: Secureface: face template protection. IEEE Trans. Inf. Forensics Secur. 16, 262–277 (2020)

    CrossRef  Google Scholar 

  19. Mai, G., Lim, M.H., Yuen, P.C.: Binary feature fusion for discriminative and secure multi-biometric cryptosystems. Image Vis. Comput. 58, 254–265 (2017)

    CrossRef  Google Scholar 

  20. Nandakumar, K., Jain, A.K.: Biometric template protection: bridging the performance gap between theory and practice. IEEE Signal Process. Mag. 32(5), 88–100 (2015)

    CrossRef  Google Scholar 

  21. Phillips, T., Zou, X., Li, F., Li, N.: Enhancing biometric-capsule-based authentication and facial recognition via deep learning. In: Proceedings of the 24th ACM Symposium on Access Control Models and Technologies, pp. 141–146 (2019)

    Google Scholar 

  22. Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001)

    CrossRef  Google Scholar 

  23. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE (1994)

    Google Scholar 

  24. Siddhad, G., Khanna, P., Ojha, A.: Cancelable biometric template generation using convolutional autoencoder. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds.) CVIP 2020. CCIS, vol. 1376, pp. 303–314. Springer, Singapore (2021).

    CrossRef  Google Scholar 

  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  26. Singh, A., Arora, A., Jaswal, G., Nigam, A.: Comprehensive survey on cancelable biometrics with novel case study on finger dorsal template protection. J. Bank. Financ. Technol. 4(1), 37–52 (2020).

    CrossRef  Google Scholar 

  27. Sudhakar, T., Gavrilova, M.: Multi-instance cancelable biometric system using convolutional neural network. In: 2019 International Conference on Cyberworlds (CW), pp. 287–294. IEEE (2019)

    Google Scholar 

  28. Sui, Y., Zou, X., Du, E.Y., Li, F.: Design and analysis of a highly user-friendly, secure, privacy-preserving, and revocable authentication method. IEEE Trans. Comput. 63(4), 902–916 (2013)

    CrossRef  MathSciNet  Google Scholar 

  29. Talreja, V., Valenti, M.C., Nasrabadi, N.M.: Multibiometric secure system based on deep learning. In: 2017 IEEE Global Conference on Signal and Information Processing (globalSIP), pp. 298–302. IEEE (2017)

    Google Scholar 

  30. Wayman, J.L., Jain, A.K., Maltoni, D., Maio, D.: Biometric Systems: Technology, Design and Performance Evaluation. Springer Science & Business Media, Heidelberg (2005).

  31. Zhang, D.: Polyu palmprint database. Biometric Research Centre, Hong Kong Polytechnic University (2006).

  32. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Pritee Khanna .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bamoriya, P., Siddhad, G., Khanna, P., Ojha, A. (2022). Cancelable Template Generation Using Convolutional Autoencoder and RandNet. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11345-1

  • Online ISBN: 978-3-031-11346-8

  • eBook Packages: Computer ScienceComputer Science (R0)