Open Journal Systems

Multimodal biometric authentication algorithm at score level fusion using hybrid optimization

E. Sujatha Nil1, A. Chilambuchelvan 2

Article ID: 415
Vol 2, Issue 1, 2018, Article identifier:

VIEWS - 330 (Abstract) 474 (PDF)


Biometric is emerging technology in identification and authentication of human being with more reliable and accurate. Combining multiple biometric systems is a promising solution to provide more security. It eliminates the disadvantages of unimodal biometric systems such as non-universality, noise in sensed data, intra-class variations, distinctiveness, spoof attacks and traditional method of authenticating a human and their identity. The proposed method depicts a multimodal biometric algorithm is designed to recognize individuals for robust and secured authentication using normalized score level fusion techniques with hybrid Genetic Algorithm and Particle Swarm Optimization for optimization in order to reduce False Acceptance Rate and False Rejection Rate and to enhance Equal Error Rate and Accuracy. 


multimodal biometrics; authentication algorithm; score level fusion; optimization

Full Text:



Algorithm, Kalyan, Veeramachaneni, et al. An adaptive multimodal biometric management—part C: Applications and reviews. IEEE Transactions On Systems, Man, And Cybernetics 2005; 35(3): 344-356.

Hong L, Jain A. Integrating faces and fingerprints for personal identification. IEEE Trans. Pattern Anal. Machine Intell 1998; 20(12): 1295–1307.

Frischholz RW, Deickmann U. A multimodal biometric identification system. IEEE Comput 2000; 33(2).

Hong L, Jain AK, Panikanti S. Can multibiometrics improve perfomance? Proc. AutoID, Summit, NJ 1999; 10: 59–64.

Eberhart R, Shi Y. Comparison between genetic algorithms and particle swarm optimization. 7th Annual Conf. Evolutionary Programming, San Diego, CA 1998; 3.

Vatsa M, Singh R, Gupta P. Comparison of iris recognition algorithms. Mayank Vatsa Department of CSE ZIT Kanpur, India mayank-richa@ 0-7803-8243-91041$17.000 IEEE 2004: 354-358.

Yin Y, Liu L, Sun X, et al. SDUMLA-HMT: A multimodal biometric database (Eds.): CCBR 2011, LNCS 7098 © Springer-Verlag Berlin Heidelberg 2011: 260–268.

Comparative study of multimodal biometric recognition by fusion of Iris and fingerprint houda benaliouche. Mohamed Touahria Scientific World Journal doi: 10.1155/2014/829369 2014 ;(6).

Dalila C, Imane H, Amine NA. Multimodal score-level fusion using Hybrid GA-PSO for Multibiometric System. Cherifi Dalila and Hafnaoui Imane, Informatica 39 2015: 209–216.

Shruthi BM, Pooja M, Mallinath, et al. Multimodal biometric authentication combining finger vein and finger print. International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, 2013; 7(10): 43-54 .

Mahri N, Suandi SAS, Rosdi BA. Finger vein recognition algorithm using phase only correlation Nurhafizah Mahri†, Shahrel Azmin Sundi @Suandi and Bakhtiar Affendi Rosdi 978-1-4244-7065-5/10/$26.00 ©2010 IEEE 2010:1–6.

Prakash CS, Anupam A, Kamta NM, et al. I.J. Information technology and computer science. Published Online January 2013 in MECS (http://www.mecs DOI: 10.5815/ijitcs.2013.02.10 Fingerprints, Iris and DNA Features based Multimodal Systems: A Review 2013; 02: 88-111.

National science and technology council. Committee on Technology, Committee on Homeland Security, Subcommittee on Biometrics.

Poh N, Bengio S, Database. Protocol and tools for evaluating score-level fusion algorithms in biometric authentication. Pattern Recognition 2005; 39(2):223–233.

(330 Abstract Views, 474 PDF Downloads)


  • There are currently no refbacks.

Copyright (c) 2018 Wireless Communication Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.