Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Sandip Kumar Singh Modak, Vijay Kumar Jha

Sandip Kumar Singh Modak and Vijay Kumar Jha. Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion. International Journal of Computer Applications 170(6):33-38, July 2017. BibTeX

	author = {Sandip Kumar Singh Modak and Vijay Kumar Jha},
	title = {Enhancing Performance of Multibiometric System using Ant Colony Optimization based on Score Level Fusion},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {6},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {33-38},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017914882},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Unimodal biometric systems have several inherent problems such as intra-class variation, noisy-sensor data, spoofing attacks and non-universality. To overcome this limitation multibiometric is a good option where we can use two or more individual modalities. In this paper we propose a multibiometric system to enhance the performance and minimize the error rate using Ant colony optimization (ACO) based on score level fusion. This work extracts the feature from two different modalities namely face and iris (left/right). In this work we use ACO as an optimization technique to select the fusion parameter like weights for the different biometric matcher and fusion rule which is used further for score level fusion. The experimental results show that the multibiometric system using ACO based on sum rule is outperform than the other fusion rule like product, tanh and exponential sum.


  1. A.K.Jain, A.Ross, and S.Prabhakar. “An Introduction to biometric recognition”, IEEE Transaction on Circuits and Systems for Video Technology. Special Issue on Image and Video-Based Biometrics, vol.14 (1):pp.4-20, 2004.
  2. Anil Jain,Karthik Nandakumar, Arun Ross,” Score Normalization in Multimodal Biometric System”, journal of Pattern Recognition, Vol.38,pp.2270-2285,2005.
  3. He, Mingxing, et al. "Performance evaluation of score level fusion in multimodal biometric systems”, Pattern Recognition, vol. 43.5, pp: 1789-1800, 2010.
  4. Hanmandlu, Madasu, et al. "Score level fusion of multimodal biometrics using triangular norms", Pattern recognition letters, vol. 32.14, pp: 1843-1850, 2011.
  5. Nandakumar, Karthik, et al. "Likelihood ratio-based biometric score fusion”, IEEE transactions on pattern analysis and machine intelligence, vol.30.2, pp: 342-347, 2008.
  6. Mezai, Lamia, and Fella Hachouf. "Score-level fusion of face and voice using particle swarm optimization and belief functions", IEEE Transactions on Human-Machine Systems, vol. 45.6, pp: 761-772, 2015.
  7. Kumar, Amioy, et al. "Decision level biometric fusion using Ant Colony Optimization", Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
  8. Tronci, Roberto, Giorgio Giacinto, and Fabio Roli. "Dynamic score selection for fusion of multiple biometric matchers", Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on. IEEE, 2007.
  9. Veeramachaneni, Kalyan, Lisa Ann Osadciw, and Pramod K. Varshney. "An adaptive multimodal biometric management algorithm”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) ,vol.35.3, pp: 344-356,2005.
  10. Srinivas, Nisha, Kalyan Veeramachaneni, and Lisa Ann Osadciw. "Fusing correlated data from multiple classifiers for improved biometric verification", Information Fusion, 2009. FUSION'09. 12th International Conference on. IEEE, 2009.
  11. Raghavendra, Ramachandra, et al. "Particle swarm optimization based fusion of near infrared and visible images for improved face verification”, Pattern Recognition, vol. 44.2, pp: 401-411, 2011.
  12. Kumar, Ajay, Vivek Kanhangad, and David Zhang. "A new framework for adaptive multimodal biometrics management", IEEE transactions on Information Forensics and Security, vol. 5.1, pp: 92-102, 2010.
  13. Kumar, Amioy, and Ajay Kumar. "Adaptive management of multimodal biometrics fusion using ant colony optimization", Information Fusion, vol. 32, pp: 49-63, 2016.
  14. Kumar, Amioy, Madasu Hanmandlu, and H. M. Gupta. "Ant colony optimization based fuzzy binary decision tree for bimodal hand knuckle verification system”, Expert Systems with Applications, vol. 40.2, pp: 439-449, 2013.
  15. Giot, Romain, Mohamad El-Abed, and Christophe Rosenberger. "Fast learning for multibiometric systems using genetic algorithms", High Performance Computing and Simulation (HPCS), 2010 International Conference on. IEEE, 2010.
  16. Cherifi, Dalila, Imane Hafnaoui, and Amine Nait Ali. "Multimodal score-level fusion using hybrid ga-pso for multibiometric system." (2015).
  17. Alford, Aniesha, et al. "GEC-based multi-biometric fusion", Evolutionary Computation (CEC), 2011 IEEE Congress on. IEEE, 2011.
  18. Dorigo, Marco, and Luca Maria Gambardella. "Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE Transactions on evolutionary computation, vol. 1.1, pp: 53-66, 1997.
  19. Huang, Chen, Xiaoqing Ding, and Chi Fang. "Pose robust face tracking by combining view-based AAMs and temporal filters", Computer Vision and Image Understanding, vol.116.7, pp: 777-792, 2012.
  20. Pujol, Pere, Dusan Macho, and Climent Nadeu. "On real-time mean-and-variance normalization of speech recognition features", Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. Vol. 1. IEEE, 2006.
  21. Daugman, John G. "High confidence visual recognition of persons by a test of statistical independence”, IEEE transactions on pattern analysis and machine intelligence, vol.15.11, pp: 1148-1161, 1993.
  22. Wildes, Richard P. "Iris recognition: an emerging biometric technology”, Proceedings of the IEEE, vol. 85.9, pp: 1348-1363, 1997.
  23. Biometric Ideal Test. Available online: (assessed on 30 August 2013).


Unimodal, multibiometric, ant colony optimization, fingerprint, iris, particle swarm optimization, score level fusion.