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2000
Volume 18, Issue 7
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems.

Methods

In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called RestNet50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature.

Results

The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method.

Conclusion

The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

© 2025 Bentham Science Publishers
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2024-05-10
2025-09-26

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References

  1. JainA.K. RossA.A. NandaK.K. Introduction to Biometrics.Berlin, GermanySpringer Science & Business Media201110.1007/978‑0‑387‑77326‑1
     [Google Scholar]
  2. LuminiA. NanniL. Overview of the combination of biometric matchers.Inf. Fusion201733718510.1016/j.inffus.2016.05.003
     [Google Scholar]
  3. ShaheedK. LiuH. YangG. QureshiI. GouJ. YinY. A systematic review of finger vein recognition techniques.Informatics (MDPI)20189213
     [Google Scholar]
  4. OloyedeM. HanckeG. Unimodal and multimodal biometric sensing systems: A review.IEEE Access201647532755510.1109/ACCESS.2016.2614720
     [Google Scholar]
  5. BouzouinaY. HamamiL. Multimodal biometric: Iris and face recognition based on feature selection of iris with GA and scores level fusion with SVM. Proceedings of the 2017 2nd International Conference on Bio-Engineering for Smart TechnologiesParis, France, 30 August–1 September 2017, pp. 1-7, 2017.10.1109/BIOSMART.2017.8095312
     [Google Scholar]
  6. HezilN. BoukroucheA. Multimodal biometric recognition using human ear and palmprint.IET Biom.20176535135910.1049/iet‑bmt.2016.0072
     [Google Scholar]
  7. VeluchamyS. KarlmarxL.R. System for multimodal biometric recognition based on finger knuckle and finger vein using feature‐level fusion and k‐support vector machine classifier.IET Biom.20176323224210.1049/iet‑bmt.2016.0112
     [Google Scholar]
  8. ChanukyaP.S.V.V.N. ThivakaranT.K. Multimodal biometric cryptosystem for human authentication using fingerprint and ear.Multimedia Tools Appl.2020791-265967310.1007/s11042‑019‑08123‑w
     [Google Scholar]
  9. AmmourB. BoubchirL. BoudenT. RamdaniM. Face–iris multimodal biometric identification system.Electronics2020918510.3390/electronics9010085
     [Google Scholar]
  10. ZhangQ. YangL.T. ChenZ. LiP. A survey on deep learning for big data.Inf. Fusion20184214615710.1016/j.inffus.2017.10.006
     [Google Scholar]
  11. PanasiukP. SzymkowskiM. MarcinD. A multimodal biometric user identification system based on keystroke dynamics and mouse movements.Computer Information Systems and Industrial Management SaeedK. HomendaW. Lecture Notes in Computer ScienceSpringer, Cham2016984267268110.1007/978‑3‑319‑45378‑1_58
     [Google Scholar]
  12. DingC. TaoD. TaoD. Robust face recognition via multimodal deep face representation.IEEE Trans. Multimed.201517112049205810.1109/TMM.2015.2477042
     [Google Scholar]
  13. Al-WaisyA.S. QahwajiR. IpsonS. Al-FahdawiS. NagemT.A.M. A multi-biometric iris recognition system based on a deep learning approach.Pattern Anal. Appl.201821378380210.1007/s10044‑017‑0656‑1
     [Google Scholar]
  14. Al-WaisyA.S. QahwajiR. IpsonS. Al-FahdawiS. A multimodal biometrie system for personal identification based on deep learning approaches.Proceedings of the 2017 Seventh International Conference on Emerging Security TechnologiesCanterbury, UK, 06-08 September 2017, pp. 163-168, 2017.10.1109/EST.2017.8090417
     [Google Scholar]
  15. SoleymaniS. DaboueiA. KazemiH. DawsonJ. NasrabadiN.M. Multi-level feature abstraction from convolutional neural networks for multimodal biometric identification. Proceedings of the 2018 24th International Conference on Pattern RecognitionBeijing, China, 20-24 August 2018, pp. 3469-3476, 201810.1109/ICPR.2018.8545061
     [Google Scholar]
  16. SoleymaniS. TorfiA. DawsonJ. NasrabadiN.M. Generalized bilinear deep convolutional neural networks for multimodal biometric identification. Proceedings of the 2018 25th IEEE International Conference on Image ProcessingAthens, Greece, 07-10 October 2018, pp. 763-767, 2018.10.1109/ICIP.2018.8451532
     [Google Scholar]
  17. GunasekaranK. RajaJ. PitchaiR. Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images.Automatika201960325326510.1080/00051144.2019.1565681
     [Google Scholar]
  18. KimW. SongJ.M. ParkK.R. Multimodal biometric recognition based on convolutional neural network by the fusion of finger-vein and finger shape using near-infrared (NIR) camera sensor.Sensors2018187229610.3390/s1807229630011970
     [Google Scholar]
  19. LiuW. LiW. SunL. ZhangL. ChenP. Finger vein recognition based on deep learning. Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and ApplicationsSiem Reap, Cambodia, 18-20 June 2017, pp. 205-210, 2017.10.1109/ICIEA.2017.8282842
     [Google Scholar]
  20. BoucheritI. ZmirliM.O. HentabliH. RosdiB.A. Finger vein identification using deeply-fused convolutional neural network.J. King Saud Univ. Comput. Inf. Sci.202034364665610.1016/j.jksuci.2020.04.002
     [Google Scholar]
  21. SchmidhuberJ. Deep learning in neural networks: An overview.Neural Netw.2015618511710.1016/j.neunet.2014.09.00325462637
     [Google Scholar]
  22. CholletF. Deep Learning with Python.New York, NY, USAManning Publications2018
     [Google Scholar]
  23. NguyenK. FookesC. RossA. SridharanS. Iris recognition with off-the-shelf CNN features: A deep learning perspective.IEEE Access20186188481885510.1109/ACCESS.2017.2784352
     [Google Scholar]
  24. AlayN. Al-BaityH.H. A multimodal biometric system for personal verification based on different level fusion of iris and face traits.Biosci. Biotechnol. Res. Commun.201912356557610.21786/bbrc/12.3/3
     [Google Scholar]
  25. YinY. LiuL. SunX. SDUMLA-HMT: A multimodal biometric database. SunZ. LaiJ. ChenX. TanT. Biometric RecognitionLecture Notes in Computer ScienceSpringer, Berlin, Heidelberg2011709826026810.1007/978‑3‑642‑25449‑9_33
     [Google Scholar]
  26. KumarA. PassiA. Comparison and combination of iris matchers for reliable personal authentication.Pattern Recognit.20104331016102610.1016/j.patcog.2009.08.016
     [Google Scholar]
  27. RahulM KohliN AgarwalR MishraS Facial expression recognition using geometric features and modified hidden Markov model.Int. J. Grid Util. Comput.201910548849610.1504/IJGUC.2019.102018
     [Google Scholar]
  28. JyotiA. YadavV. RahulM. Blockchain security attacks, difficulty, and prevention.Recent Adv. Electr. Electron. Eng.2023
     [Google Scholar]
  29. RahulMayur TiwariNamita PrakashAyushi YadavVikash Garment defect detection system based on histogram using deep learningDoSCI 2023: Proceedings of Fourth Doctoral Symposium on Computational Intelligencepp. 255-265, 2023.10.1007/978‑981‑99‑3716‑5_22
     [Google Scholar]
  30. RahulMayur ShuklaRati TyagiDevvrat TiwariNamita YadavVikash A new hybrid approach for efficient emotion recognition using deep learning.Int. J. Electr. Electron. Res.2023101182210.37391/IJEER.100103
     [Google Scholar]
  31. RahulM. PalP. YadavV. DellwarD.K. SinghS. Impact of similarity measures in K-means clustering method used in movie recommender systems.IOP Conf. Series Mater. Sci. Eng.20211022101210110.1088/1757‑899X/1022/1/012101
     [Google Scholar]
  32. AlayN. Al-BaityH.H. Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits.Sensors20202019552310.3390/s2019552332992524
     [Google Scholar]
  33. SalkićH. KvasinaM. SalkićA. RistićV. Use of face recognition software by karhunen love method.Adv. Engin. Lett.2022139810710.46793/adeletters.2022.1.3.4
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965293198240418063311
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Feature Level Fusion of Fingerprints and IRIS for the Enhancement of Biometric System
Recent Advances in Electrical & Electronic Engineering 18, 984 (2025); https://doi.org/10.2174/0123520965293198240418063311
/content/journals/raeeng/10.2174/0123520965293198240418063311
/content/journals/raeeng/10.2174/0123520965293198240418063311
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  • Article Type:     Research Article
Keyword(s): fingerprints; IRIS; multilayer perceptrons; Multimodal; RestNet50; SDUMLA-HMT

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