Skip to main content
SpringerLink
Log in

Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Biometric recognition systems are widely being used in several applications due to its distinctiveness and reliability. In recent years, hand-based person recognition has received much momentum due to its stability, feature richness, reliability and higher user acceptability. In this paper, we propose a multimodal hand biometric system based on Finger Knuckle Print (FKP) and Palmprint. In particular, the PCANet deep learning method is employed to extract distinctive features from each modality. Then, multiclass SVM is utilized to compute a matching score for each individual modality. Finally, score level fusion is performed to combine the matching scores via different rules such as, min, sum, max and multiplication. The performance of the proposed system is evaluated on the publicly available database known as PolyU. First, we conducted several experiments on single FKP and Palmprint traits. Next, score level fusion based multimodal experiments were performed. The proposed framework was able to achieve 0.00% of EER (equal error rates) and 100% of rank-1 performance. In addition, the proposed system based on PCANet with score level fusion of FKP and Palmprint outperformed existing multimodal methods.

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
Fig. 10

Similar content being viewed by others

References

  1. Akhtar Z, Fumera G, Marcialis GL, Roli F (2011) Robustness analysis of likelihood ratio score fusion rule for multimodal biometric systems under spoof attacks. In: Carnahan Conference on Security Technology, pp 1–8

  2. Ali A, Zhu Y, Chen Q, Yu J, Cai H (2019) Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp 125–132

  3. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80(20):31401–31433

    Article  Google Scholar 

  4. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci (Ny) 577:852–870

    Article  MathSciNet  Google Scholar 

  5. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247

    Article  Google Scholar 

  6. Attallah B, Brik Y, Chahir Y, Djerioui M, Boudjelal A (2019) Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF. In: 6th International Conference on Image and Signal Processing and their Applications (ISPA), pp 1–5

  7. Attia A, Chaa M, Akhtar Z, Chahir Y (2020) Finger kunckcle patterns based person recognition via bank of multi-scale binarized statistical texture features. Evol Syst 11(4):625–635

    Article  Google Scholar 

  8. Attia A, Akhtar Z, Chalabi NE, Maza S, Chahir Y (2021) Deep rule-based classifier for finger knuckle pattern recognition system. Evol Syst 12(4):1015–1029

    Article  Google Scholar 

  9. Attia A, Akhtar Z, Chahir Y (2021)Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication. Signal Image Video Process 15(4):851–859

    Article  Google Scholar 

  10. Bastanfard A, Karam H, Takahashi H, Nakajima M (2002) Simulation of human facial aging and skin senility (Session1 Computer Graphics)(IWAIT2002). In: ITE Technical Report 26.3, pp 1–7

  11. Bastanfard A, Bastanfard O, Takahashi H, Nakajima M (2004) Toward anthropometrics simulation of face rejuvenation and skin cosmetic. Comput Animat Virtual Worlds 15:3–4

    Article  Google Scholar 

  12. Benmalek M, Attia A, Bouziane A, Hassaballah M (2022) A semi-supervised deep rule-based classifier for robust finger knuckle-print verification. Evol Syst :1–12

  13. Bhattacharya S et al (2021) Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustain Cities Soc 65:102589

    Article  Google Scholar 

  14. Chaa M, Boukezzoula N-E, Attia A (2017)Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems. J Electron Imaging 26(1):13018

    Article  Google Scholar 

  15. Chaa M, Akhtar Z, Attia A (2019) 3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier. IET Image Process 13(5):736–745

    Article  Google Scholar 

  16. Chan T-H, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: A simple deep learning baseline for image classification? IEEE Trans image Process 24(12):5017–5032

    Article  MathSciNet  Google Scholar 

  17. Chlaoua R, Meraoumia A, Aiadi KE, Korichi M (2019) Deep learning for finger-knuckle-print identification system based on PCANet and SVM classifier. Evolving Systems 10(2):261–272

    Article  Google Scholar 

  18. Connie T, Teoh A, Goh M, Ngo D (2003) Palmprint recognition with PCA and ICA. In: Proc Image and Vision Computing, New Zealand

  19. Cortes C, Vapnik V (1995)Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  20. Database PolyUP, Database “PolyUP (2011) ” [Online]. Available: http://www.comp.polyu.edu.hk/biometrics/palmprint.htm

  21. Dehshibi MM, Bastanfard A (2010) A new algorithm for age recognition from facial images. Sig Process 90(8):2431–2444

    Article  Google Scholar 

  22. Gadekallu TR, Rajput DS, Reddy M, Lakshmanna K, Bhattacharya S, Singh S ...  Alazab M (2021) A novel PCA--whaleoptimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Process 18(4):1383–1396

  23. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1):4–20

  24. Jaswal G, Poonia RC (2021) Selection of optimized features for fusion of palm print and finger knuckle-based person authentication. Expert Syst 38(1):e12523

    Article  Google Scholar 

  25. Jaswal G, Kaul A, Nath R (2016) Knuckle print biometrics and fusion schemes--overview, challenges, and solutions. ACM Comput Surv 49(2):34

    Article  Google Scholar 

  26. Jaswal G, Nigam A, Nath R (2017) DeepKnuckle: revealing the human identity. Multimed Tools Appl 76(18):18955–18984

    Article  Google Scholar 

  27. Jaswal G, Kaul A, Nath R (2017) Palmprint and finger knuckle based person authentication with random forest via kernel-2DPCA. In: International Conference on Pattern Recognition and Machine Intelligence, pp 233–240

  28. Kant C, Chaudhary S (2021) A multimodal biometric system based on finger knuckle print, fingerprint, and palmprint traits. In: Innovations in Computational Intelligence and Computer Vision. Springer, Berlin, pp 182–192

  29. Lakshmanan S, Velliyan P, Attia A, Chalabi NE (2022) Finger knuckle pattern person authentication system based on monogenic and LPQ features. Pattern Anal Appl: 1–13

  30. Meraoumia A, Chitroub S, Bouridane A (2011) Palmprint and Finger-Knuckle-Print for efficient person recognition based on Log-Gabor filter response. Analog Integr Circuits Signal Process 69(1):17–27

    Article  Google Scholar 

  31. Nigam A, Gupta P (2015) Designing an accurate hand biometric based authentication system fusing finger knuckleprint and palmprint. Neurocomputing 151:1120–1132

    Article  Google Scholar 

  32. Oveisi IS, Modarresi M (2015) A feature level multimodal approach for palmprint and knuckleprint recognition using AdaBoost classifier. In: International Conference and Workshop on Computing and Communication (IEMCON), pp 1–7

  33. Esther Rani P, Shanmugalakshmi R (2014) Multimodal biometric system using score level fusion of palmprint and finger knuckle print. Int J Adv Res Comput Sci 5(6)

  34. PolyU (2010) The Hong Kong polytechnic university (PolyU)Finger-Knuckle-Print Database, [Online]. Available: http://www.comp.polyu.edu.hk/biometrics/FKP.html

  35. Usha K, Ezhilarasan M (2016) Personal recognition using finger knuckle shape oriented features and texture analysis. J King Saud Univ Inf Sci 28(4):416–431

    Google Scholar 

  36. Usha K, Ezhilarasan M (2016) Fusion of geometric and texture features for finger knuckle surface recognition. Alex Eng J 55(1):683–697

    Article  Google Scholar 

  37. Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138

  38. Veluchamy S, Karlmarx LR (2020)HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition. Sens Rev

  39. Xinshao W, Cheng C (2015) Weed seeds classification based on PCANet deep learning baseline. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp 408–415

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

    Article  Google Scholar 

  41. Zhang L, Zhang L, Zhang D, Zhu H (2010) Online finger-knuckle-print verification for personal authentication. Pattern Recognit 43(7):2560–2571

    Article  Google Scholar 

  42. Zhu L, Zhang S (2010) Multimodal biometric identification system based on finger geometry, knuckle print and palm print. Pattern Recognit Lett 31(12):1641–1649

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MSE Laboratory, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria

    Abdelouahab Attia & Sofiane Mazaa

  2. Computer Science Department, Mohamed El Bachir El Ibrahimi University, Bordj Bou Arreridj, Algeria

    Abdelouahab Attia & Sofiane Mazaa

  3. State University of New York Polytechnic Institute, Utica, NY, USA

    Zahid Akhtar

  4. Image Team GREYC-CNRS UMR, University of Caen, Caen, France

    Youssef Chahir

Authors
  1. Abdelouahab Attia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sofiane Mazaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zahid Akhtar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Youssef Chahir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelouahab Attia.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Attia, A., Mazaa, S., Akhtar, Z. et al. Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system. Multimed Tools Appl 81, 10961–10980 (2022). https://doi.org/10.1007/s11042-022-12384-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12384-3

Keywords

Search

Navigation