Skip to main content
SpringerLink
Log in

An evaluation of denoising techniques and classification of biometric images based on deep learning

  • 1207: Innovations in Multimedia Information Processing & Retrieval
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A biometric system uses pattern recognition to identify an individual based on various traits such as the face, fingerprints, etc. Adverse or uncontrolled conditions can often lead to the introduction of noise in data. In this paper, we evaluate the impact of noise on the quality of images that are input into a biometric system. Further, we analyze the role of various filters in the denoising of images. The paper proposes a robust deep learning-based framework in Pytorch to classify denoised images, which is validated by evaluating performance metrics on two popular benchmarks. Our study shows that our proposed framework helps achieve state-of-the-art accuracy without any trade-off caused due to the introduction of noise in images.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Adiga S, Sivaswamy J (2019) Fpd-m-net: Fingerprint image denoising and inpainting using m-net based convolutional neural networks. Inpainting and Denoising Challenges, pp 51–61

  2. Abbaas F, Serpen G (2020) Evaluation of biometric user authentication using an ensemble classifier with face and voice recognition. arXiv preprint arXiv:2006.00548

  3. Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2011) Quality measures in biometric systems. IEEE Security & Privacy 10(6), 52–62

    Google Scholar 

  4. Alqahtani A (2014) Valuation of the impact of noise on iris recognition biometric authentication systems. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV)

  5. Angle S, Bhagtani R, Chheda H (2005) Biometrics: A further echelon of security. In: UAE International Conference on Biological and Medical Physics, Citeseer

  6. Azzeh J, Zahran B, Alqadi Z (2018) Salt and pepper noise: Effects and removal. Salt and pepper noise: Effects and removal 2(4):252–256

    Google Scholar 

  7. Babich A (2012) Biometric authentication. Types of Biometric Identifiers, pp 56

  8. Bharadwaj S, Vatsa M, Singh R (2014) Biometric quality: a review of fingerprint, iris, and face. EURASIP journal on Image and Video Processing 2014(1):34

    Article  Google Scholar 

  9. Bajaj K, Singh D, Ansari M (2020) Autoencoders based deep learner for image denoising. Procedia Computer Science 171:1535–1541

    Article  Google Scholar 

  10. Buades A, Coll B, Morel JM (2011) Non-local means denoising. Image Processing On Line 1:208–212

    Article  MATH  Google Scholar 

  11. Cherifi D, Radji N, Nait-Ali A (2011) Effect of noise, blur and motion on global appearance face recognition based methods performance. Int J Comput Appl 16(6), 4–13

    Google Scholar 

  12. Delac K, Grgic M (2004) A survey of biometric recognition methods. In: Proceedings. Elmar-2004. 46th International Symposium on Electronics in Marine, IEEE, pp 184–193

  13. Deshpande RG, Ragha LL, Sharma SK (2018) Video quality assessment through psnr estimation for different compression standards. Indonesian Journal of Electrical Engineering and Computer Science 11(3), 918–924

    Article  Google Scholar 

  14. Dorizzi B (2005) Biometrics at the frontiers, assessing the impact on society technical impact of biometrics. Background paper for the Institute of Prospective Technological Studies, DG JRC-Sevilla, European Commission

    Google Scholar 

  15. Dupé F-X, Fadili JM, Starck J-L (2009) A proximal iteration for deconvolving poisson noisy images using sparse representations. IEEE Transactions on Image Processing 18(2), 310–321

    Article  MathSciNet  MATH  Google Scholar 

  16. Hammod DN (2020) Biometric authentication based on hash iris features. International Journal of Biometrics and Bioinformatics (IJBB) 13(1):1

    Google Scholar 

  17. Hore A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. In: 20th International Conference on Pattern Recognition, IEEE 2010, pp 2366–2369

  18. Ilesanmi A, Ilesanmi T (2021) Methods for image denoising using convolutional neural network: a review. Complex and Intelligent Systems

  19. Jan F, Min-Allah N (2020) An effective iris segmentation scheme for noisy images. Biocybernetics and Biomedical Engineering 40(3), 1064–1080

    Article  Google Scholar 

  20. Jain AK, Pankanti S, Prabhakar S, Hong L, Ross A (2004) Biometrics: a grand challenge. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol 2. IEEE, pp 935–942

  21. Jeon W, Jeong W, Son K, Yang H (2018) Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks. Optics letters 43(17):4240–4243

    Article  Google Scholar 

  22. Mahfouz A, Mahmoud TM, Eldin AS (2017) A survey on behavioral biometric authentication on smartphones. Journal of information security and applications 37:28–37

    Article  Google Scholar 

  23. Prabhu R, Yu X, Wang Z, Liu D, Jiang A (2019) U-finger: Multi-scale dilated convolutional network for fingerprint image denoising and inpainting. Inpainting and Denoising Challenges. pp 45–50

  24. Pandey AN, Singh AV (2020) A model for real-time biometric authentication using facial and hand gesture recognition. In: Innovations in Computer Science and Engineering, Springer, pp 93–99

  25. Rathgeb C (2008) Iris-based biometric cryptosystems. Springer

  26. de Santos A, Avila CS, Bailador G, Guerra J (2011) Study of the effect of blurriness in image acquisition for hand biometrics in mobile devices. In: 2011 Carnahan Conference on Security Technology, IEEE, pp 1–7

  27. Sardy S, Tseng P, Bruce A (2001) Robust wavelet denoising. IEEE Transactions on Signal Processing 49(6), 1146–1152

    Article  Google Scholar 

  28. Saini R, Rana N (2014) Comparison of various biometric methods. Int J Adv Sci Technol 2(1), 24–30

    Google Scholar 

  29. Singh A, Porikli F, Ahuja N (2014) Super-resolving noisy images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2846–2853

  30. Soni KBR, Chand S (2019) Optimal feature and classifier selection for text region classification in natural scene images using weka tool. Multimed Tools Appl 78:31757-31791

  31. Soni KB (2019) Text detection and localization in natural scene images based on text awareness score. Appl Intell 49:1376–1405

    Article  Google Scholar 

  32. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena 60(1–4):259–268

    Article  MathSciNet  MATH  Google Scholar 

  33. Ramli DA, Samad SA, Hussain A (2010) Performances of speech signal biometric systems based on signal to noise ratio degradation. In: Computational Intelligence in Security for Information Systems, Springer 2010, pp 73–80

    Google Scholar 

  34. Russo F (2003) A method for estimation and filtering of gaussian noise in images. IEEE Transactions on Instrumentation and Measurement 52(4), 1148–1154

    Article  Google Scholar 

  35. Pflug A, Wagner J, Rathgeb C, Busch C (2014) Impact of severe signal degradation on ear recognition performance. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE 2014, pp 1342–1347

  36. Venkatesh S, Ramachandra R, Raja K, Spreeuwers L, Veldhuis R, Busch C (2019) Morphed face detection based on deep color residual noise. In: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)

  37. Wagner J , Pflug A, Rathgeb C, Busch C (2004) Effects of severe signal degradation on ear detection. In: 2nd International Workshop on Biometrics and Forensics, IEEE, pp 1–6

  38. Xu S, Zhang C, Zhang J (2020) Bayesian deep matrix factorization network for multiple images denoising. Neural Networks 123:420–428

    Article  Google Scholar 

  39. Zeng J, Qiu X, Shi S (2019) Image processing effects on the deep face recognition system. Mathematical Biosci Eng 18(2), 1187–1200

    Article  MATH  Google Scholar 

  40. Zhang Y, Tian X, Ren P (2014) An adaptive bilateral filter based framework for image denoising. Neurocomputing 140:299–316

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Netaji Subhas Institute of Technology, New Delhi, India

    Shefali Arora & M. P. S. Bhatia

  2. Ganga Institute of Technology and Management, Haryana, Jhajjar, India

    Ruchi Mittal

  3. Indira Gandhi Delhi Technical University For Women, New Delhi, India

    Harshita Kukreja

Authors
  1. Shefali Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruchi Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harshita Kukreja
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. P. S. Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shefali Arora.

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

Arora, S., Mittal, R., Kukreja, H. et al. An evaluation of denoising techniques and classification of biometric images based on deep learning. Multimed Tools Appl 82, 8287–8302 (2023). https://doi.org/10.1007/s11042-021-11573-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11573-w

Keywords

Search

Navigation