Skip to main content
SpringerLink
Log in

Boosting hand vein recognition performance with the fusion of different color spaces in deep learning architectures

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Human recognition and authentication through biometrics generally rely on feature extraction from images of physiological traits. These images can be represented in different color models. This study represents human palm vein images in five different color spaces (RGB, XYZ, LAB, YUV, and HSV) and combines their decisions in CNN models for person recognition. Color spaces are generally represented in three channels. This study identifies the channel with the highest contribution to pattern recognition in images and proposes to use only this channel per color space in the identification process instead of all three channels. The experiments confirm that channels representing how humans perceive colors are generally mostly responsible for features extracted from vein pattern biometrics. The proposed architecture is tested using modified AlexNet, VGG-19, and ResNet-50 Convolutional Neural Network (CNN) models on palm vein datasets from the FYO, PUT, and VERA databases. Experimental results showed considerable improvement in palm vein recognition compared to similar studies.

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
Fig. 6

Similar content being viewed by others

Availability of data and materials

FYO palm vein database is publicly available on https://fyo.emu.edu.tr/en. Sources for the PUT Vein Pattern Database and the VERA Palmvein Database are provided in the manuscript.

References

  1. Kavitha, S., Sripriya, P.: A review on palm vein biometrics. Int. J. Eng. Technol. 7, 407 (2018). https://doi.org/10.14419/ijet.v7i3.6.16013

    Article  Google Scholar 

  2. Wu, W., Elliott, S.J., Lin, S., Sun, S., Tang, Y.: Review of palm vein recognition. IET Biom. 9(1), 1–10 (2019). https://doi.org/10.1049/iet-bmt.2019.0034

    Article  Google Scholar 

  3. Tome, P., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Fusion of Facial Regions Using Color Information in a Forensic Scenario. 8259, 399–406 (2013). https://doi.org/10.1007/978-3-642-41827-3_50

    Article  Google Scholar 

  4. Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., Jatakia, J.: (2016). Human skin detection using RGB, HSV and YCbCr color models, In: International Conference on Communication and Signal Processing 2016 (ICCASP 2016), Atlantis Press. https://doi.org/10.2991/iccasp-16.2017.51

  5. Kaya, U., Baçsaran, M.: A comparative study of classification methods on human skin detection from RGB and YCbCr represented color images. Eskiçsehir Technical Univ. J. Sci. Technol. A - Appl. Sci. Eng. 21, 40–44 (2020)

    Google Scholar 

  6. Vaghela, H., Modi, H., Pandya, M., Potdar, B.M.: Comparative study of HSV color model and Ycbcr color model to detect nucleus of white cells. Int. J. Comput. Appl. 150, 38–42 (2016). https://doi.org/10.5120/ijca2016911614

    Article  Google Scholar 

  7. Shaik, K.B., Packyanathan, G., Kalist, V., Sathish, B.S., Jenitha, J.: Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Comput. Sci. 57, 41–48 (2015). https://doi.org/10.1016/j.procs.2015.07.362

    Article  Google Scholar 

  8. Alkinani, F., Rahma, A.M.: A comparative study of KMCG segmentation based on YCbCr, RGB, and HSV color spaces. J. AL-Qadisiyah Comput. Sci. Math. 78, 789–963 (2019)

    Google Scholar 

  9. Soleimanizadeh, S., Mohamad, D., Saba, T. Rehman, A.: (2015). Recognition of Partially Occluded Objects Based on the Three Different Color Spaces (RGB, YCbCr, HSV). 3D Research. 6

  10. Aziz, M.M.: Iraqi currency recognition system using RGB and HSV color average. Int. J. Bus. Adm. Stud. 2(1), 9–15 (2016). https://doi.org/10.20469/ijbas.2.10003-1

    Article  Google Scholar 

  11. Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Hussain, S., Ullah, I., Zhang, X.: DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Syst. Appl. 191, 116288 (2022)

    Article  Google Scholar 

  12. Kuzu, R.S., Maiorana, E., Campisi, P.: On the intra-subject similarity of hand vein patterns in biometric recognition. Expert Syst. Appl. 192, 116305 (2021)

    Article  Google Scholar 

  13. Yan, X., Kang, W., Deng, F., Wu, Q.: Palm vein recognition based on multi-sampling and feature-level fusion. Neurocomputing 151, 798–807 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Ananthi, G., Sekar, J.R., Arivazhagan, S.: Human palm vein authentication using curvelet multiresolution features and score level fusion. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02253-9

    Article  Google Scholar 

  16. Fanjiang, Y., Lee, C., Du, Y., Horng, S.: Palm vein recognition based on convolutional neural network. Informatica 32(4), 687–708 (2021). https://doi.org/10.15388/21-INFOR462

    Article  MathSciNet  Google Scholar 

  17. Wu, W., Wang, Q., Yu, S., Luo, Q., Lin, S., Han, Z., Tang, Y.: Outside box and contactless palm vein recognition based on a wavelet denoising ResNet. IEEE Access 9, 82471–82484 (2021). https://doi.org/10.1109/access.2021.3086811

    Article  Google Scholar 

  18. Aberni, Y., Boubchir, L., Daachi, B.: Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization. Pattern Recognit. Lett. (2020). https://doi.org/10.1016/j.patrec.2020.05.030

  19. Sun, S., Cong, X., Zhang, P., Sun, B., Guo, X.: Palm vein recognition based on NPE and KELM. IEEE Access 9, 71778–71783 (2021)

    Article  Google Scholar 

  20. Levkowitz, H., Herman, G.T.: GLHS: A generalized lightness, Hue, and saturation color model. Graph. Models Image Process. 55(4), 271–285 (1993)

  21. Babalola, F.O., Bitirim, Y., Toygar, Ö.: Palm vein recognition through fusion of texture-based and CNN-based methods. SIViP 15, 459–466 (2021). https://doi.org/10.1007/s11760-020-01765-6

  22. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  23. Ha, I., Kim, H., Park, S., Kim, H.: Image retrieval using BIM and features from pretrained VGG network for indoor localization. Build. Environ. 140, 23–31 (2018). https://doi.org/10.1016/j.buildenv.2018.05.026

    Article  Google Scholar 

  24. Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S.: (2016). Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778

  25. Kabacinski, R., Kowalski, M.: Vein pattern database and benchmark results. Electr. Lett. 47, 1127 (2011)

    Article  Google Scholar 

  26. Tome, P., Marcel, S.: On the vulnerability of palm vein recognition to spoofing attacks, pp. 319-325. In: Proceedings of 8th IAPR International Conference on Biometrics (ICB), Pucket, Thailand (2015)

  27. Wu, W., Elliott, S.J., Lin, S., Yuan, W.: Low-cost biometric recognition system based on NIR palm vein image. IET Biom. 8(3), 206–214 (2018). https://doi.org/10.1049/iet-bmt.2018.5027

    Article  Google Scholar 

  28. Kuzu, R.S., Maiorana, E., Campisi, P.: Vein-based biometric verification using densely-connected convolutional autoencoder. IEEE Signal Process. Lett. 27, 1869–1873 (2020). https://doi.org/10.1109/LSP.2020.3030533

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Eastern Mediterranean University, 99628, Famagusta, North Cyprus, via Mersin 10, Turkey

    Felix Olanrewaju Babalola, Önsen Toygar & Yıltan Bitirim

Authors
  1. Felix Olanrewaju Babalola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Önsen Toygar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yıltan Bitirim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors made substantial contributions to the concept, design, and revision of the paper.

Corresponding author

Correspondence to Önsen Toygar.

Ethics declarations

Conflicts of interest

The authors have no conflicts of interest that might be perceived as influencing the results and/or discussions reported in this paper.

Ethical approval

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babalola, F.O., Toygar, Ö. & Bitirim, Y. Boosting hand vein recognition performance with the fusion of different color spaces in deep learning architectures. SIViP 17, 4375–4383 (2023). https://doi.org/10.1007/s11760-023-02671-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02671-3

Keywords

Search

Navigation