Skip to main content
SpringerLink
Log in

Convolutional neural network-based feature extraction using multimodal for high security application

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

An efficient biometrics-based security system is the prime need in modern security industry. Biometric modalities are unique features of any human being based on which a computer system can recognise, authenticate or verify a person. In this paper we propose a convolutional neural network-based face, fingerprint, palm vein identification system. Main purpose of this paper is to propose a convolutional neural network with minimum layers for face, fingerprint and palm vein, achieving high accuracy and reducing the complexity. The network is of two convolutional layers, two ReLU layers and two Maxpooling layesr with ten hidden layers in Fully connected layer. The dataset of 4500 images is generated for all the modalities. Dataset images are used for 60% training, 10% validation and testing 30%. Proposed CNN architecture’s accuracy is 95% for face, 94% for fingerprint and 99% palm-vein. The CNN used with minimum layers has performed consistently for all the biometric modalities maintaining good accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Jain Anil, Flynn Patrick, Ross Arun (2010) Handbook of biometrics. Springer Publishing Company, New York

    Google Scholar 

  2. Aksass B, Ouanan H, Ouanan M (2017) Novel approach to pose invariant face recognition science direct. Procedia Comput Sci 110:434–439

    Article  Google Scholar 

  3. Yang Meng, Zhang Lei, Shiu Simon C.K. (2013) Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary. Pattern Recognit 46:1865–1878

    Article  Google Scholar 

  4. Athale SS, Patil D, Deshpande P, Dandawate YH (2015) Hardware implementation of palm vein biometric modality for access control in multi-layered security system. Procedia Comput Sci 58(2015):492–498

    Article  Google Scholar 

  5. Nagar Abhishek, Nandakumar Karthik, Jain Anil K (2012) Multibiometric cryptosystems based on feature-level fusion. IEEE Trans Inf Forensics Secur 7(1):255–268

    Article  Google Scholar 

  6. Li K, Bu S (2018) Multimodal feature fusion for geographic image annotation. Pattern Recognit 73:1–14. https://doi.org/10.1016/j.patcog.2017.06.036

    Article  Google Scholar 

  7. Jain Anil K, Ross Arun (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20

    Article  Google Scholar 

  8. Bhattacharyya Debnath, Ranjan Rahul, Alisherov Farkhod (2009) Biometric Authentication: a review. Int J u- e- Serv Sci Technol 2(3):13–23

    Google Scholar 

  9. Yan Xuekui, Kang Wenxiong, Qiuxia Wu, Deng Feiqi (2015) Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing 151:798–807

    Article  Google Scholar 

  10. Ahmed T, Sharma M (2017) An advanced fingerprint matching using minutiae-based indirect local features, Springer, Science business media, LLC, part of Springer Nature, 29 Nov 2017

  11. Abe N, Shinzaki T (2015) Vectorized fingerprint representation using minutiae relation code. In: 15th IEEE conference on biometrics, pp 408–415, 19–22 May 2015

  12. Setumin S, Suandi SA (2018) Difference of gaussian oriented gradient histogram for face sketch to photo matching, IEEE Access, vol 6, 12 July 2018

  13. Huang Z, Zhu H, Zhou JT, Peng X (2018) Multiple marginal fisher analysis. IEEE Trans Ind Electron 1:1–10

    Google Scholar 

  14. LiLi JianqiangGao, Ge H (2016) A new face recognition method via semi-discrete decomposition for one sample problem. Optik 127(19):7408–7741

    Article  Google Scholar 

  15. Chang Shu, Xiaoqing Ding, Chi Fang (2011) Histogram of the oriented gradient for face recognition. Tsinghua Sci Technol 16(2):216–224

    Article  Google Scholar 

  16. Li J, Wang T, Zhang Y (2011) Face detection using SURF cascade, IEEE international conference on computer vision workshops, pp 2183–2190

  17. Zhou Yingbo, Kumar Ajay (2011) Human identification using palm-vein images. IEEE Trans Inf Forensics Secur 6(4):1259–1274

    Article  Google Scholar 

  18. Kuang-Shyr Wu, Lee Jen-Chun, Lo Tsung-Ming, Chang Ko-Chin, Changa Chien-Ping (2013) A Secure Palm vein recognition System. J Syst Softw 86(11):2870–2876

    Article  Google Scholar 

  19. Xin Ma, Xiaojun Jing (2017) Palm vein recognition method based on fusion of local Gabor histograms. J China Univ Posts Telecommun 24(6):55–66

    Article  Google Scholar 

  20. Yang Wenming, Huang Xiaola, Zhou Fei, Liao Qingmin (2014) Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. J Inf Sci 268:20–32

    Article  Google Scholar 

  21. Ahmad MI, Woo WL, Dlay S (2016) Non-stationary feature fusion of face and palmprint multimodal biometrics. Journal of Neurocomput 177:49–61

    Article  Google Scholar 

  22. Yogesh HD, Inamdar S (2016) Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application. Int. J. Biom 8(1):33–51

    Google Scholar 

  23. Oloyede MO, Hancke GP, Myburgh HC (2018) Improving face recognition systems using a new image enhancement technique Hybrid Features and the Convolutional Neural Network. IEEE Access 6:75181–75191

    Article  Google Scholar 

  24. Silva PH, Luz E, Zanlorensi LA, Menotti D Jr (2018) Gladston moreira multimodal feature level fusion based on particle swarm optimization with deep transfer learning, IEEE Conference 2018, IEEE congress evolutionary computation (CEC), pp 1–8

  25. Sajjad M, Khan S, Hussain T, Muhammad K, Sangaiah AK, Castiglione A, Esposito C, Baik SW (2019) CNN-based anti-spoofing two-tier multi-factor authentication system. Pattern Recogn Lett 126:123–131

    Article  Google Scholar 

  26. Vijay A, Kanhangad V (2020) PoreNet: CNN-based pore descriptor for high-resolution fingerprint recognition. IEEE Sens J. https://doi.org/10.1109/JSEN.2020.2987287

    Article  Google Scholar 

  27. Saraswathi K, Vimala N (2016) A novel multimodal biometrics based authentication and key exchange system, Neurocomputing, pp 608–615

  28. Rajalakshmi B, Kannammal A, Sridevi R (2011) Multimodal biometric cryptosystem involving face fingerprint and palm vein. Int J Comput Sci Issues 8(4):604–610

    Google Scholar 

  29. Shah G, Shirke S, Sawant S, Dandawate YH (2015) Palm Vein pattern based biometric recognition system. Int J Comput Appl Technol 51(2):105–111

    Article  Google Scholar 

  30. Chin J, Ong TS, Teoh ABJ, Goh KOM (2014) Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion. J Inf Fusion 18:161–174

    Article  Google Scholar 

  31. Yin X, Liu X (2018) Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans Image Process 27(2):964–975

    Article  MathSciNet  Google Scholar 

  32. Jeon Wang-Su, Rhee Sang-Yong (2017) Fingerprint Pattern Classification Using Convolution Neural Network. Int J Fuzzy Logic Intell Syst 17(3):170–176

    Article  Google Scholar 

  33. Chanta S, Hila A, Elsaleh R (2018) Palm vein biometric authentication using convolutional neural networks, Springer Nature Switzerland, SETIT, SIST 146:352–363

Download references

Author information

Authors and Affiliations

  1. Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, Maharashtra, India

    Priti Shende

  2. Vishwakarma Institute of Information Technology, Kondhwa, Pune, Maharashtra, India

    Yogesh Dandawate

Authors
  1. Priti Shende
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogesh Dandawate
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priti Shende.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shende, P., Dandawate, Y. Convolutional neural network-based feature extraction using multimodal for high security application. Evol. Intel. 14, 1023–1033 (2021). https://doi.org/10.1007/s12065-020-00522-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00522-5

Keywords

Search

Navigation