Score-Level Fusion Technique for Multi-Modal Biometric Recognition Using ABC-Based Neural Network
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Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometric system utilizes two or more individual modalities so as to improve the recognition accuracy. The key to multimodal biometrics is the fusion of the various biometric data after feature extraction. In this paper, score level fusion technique for multi-modal biometric recognition using Artificial Bee Colony (ABC) based Neural Network (NN) is proposed. The technique consists of two phases namely feature extraction phase and score fusion phase. Features are extracted from the fingerprint, face and iris modalities in the feature extraction phase. Fusion of score value is carried out after obtaining the individual matching scores from the three modalities. Fusion of scores is based on neural network where, ABC algorithm is used as a training algorithm and based on the scores obtained from ABC-based neural network, the recognition is done. The implementation is done using MATLAB and the performance of the proposed technique is evaluated using FRR, FAR, accuracy and ROC curve. The proposed technique is compared with KNN technique and from the results we can see that our proposed technique has achieved better results by having lower FRR and FAR values and higher accuracy measure.
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Kalyan Veeramachaneni, Lisa Ann Osadciw and Pramod K. Varshney, "Adaptive Multimodal Biometric Fusion Algorithm Using Particle Swarm" Proceedings of SPIE 5099, Vol. 211, 2003.
Eren Camlikaya, Alisher Kholmatov and Berrin Yanikoglu, "Multi-biometric Templates Using Fingerprint and Voice", Biometric technology for human identification, No5, Vol. 6944, Orlando FL, pp: 1-9, 2008.
A. Ross and A.K. Jain, “Information Fusion in Biometrics”, Proc. of AVBPA, Halmstad, Sweden, pp: 354-359 June 2001.
Seifedine Kadry and Khaled Smaili, "A Design and Implementation of a Wireless IRIS Recognition Attendance Management System", Information Technology and Control, Vol. 36, No. 3, pp: 323-329, 2007.
Piotr Porwik and Lukasz Wieclaw, "A new efficient method of fingerprint image enhancement", Int. J. Biometrics, Vol. 1, No. 1, pp: 36-46, 2008.
Yezeng Cheng and Kirill V. Larin, "Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis", Applied Optics, Vol. 45, No. 36, pp: 9238-9245, 2006.
J. Daugman, “High Confidence Recognition of Persons by Test Of Statistical Independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, pp. 1148-1160, 1993.
Sandipan P. Narote, Abhilasha S. Narote, Laxman M. Waghmare, “Iris Based Recognition System Using Wavelet Transform”, In proceedings of IJCSNS International Journal of Computer Science and Network Security, Vol.9, No.11, November 2009.
Ahmad M. Sarhan, “Iris Recognition Using Discrete Cosine Transform And Artificial Neural Networks”, Journal of Computer Science, Vol.5, No. 5, pp. 369-373, 2009.
Pascal Paysan, ReinhardKnothe, Brian Amberg, Sami Romdhani and Thomas Vetter, “Face Recognition Using 3-D Models: Pose and Illumination “, In proceedings of IEEE ,Vol. 94, No.11, pp.1977 - 1999, 2009.
H B Kekre and V ABharadi, “Gabor Filter Based Feature Vector for Dynamic Signature Recognition”, International Journal of Computer Applications, Vol. 2, No.3, pp.74-80, May 2010.
Mingxing He, Shi-Jinn Horng, Pingzhi Fan, Ray-Shine Run, Rong-Jian Chen, Jui-Lin Lai,Muhammad Khurram Khan, Kevin Octavius Sentosa, "Performance evaluation of score level fusion in multimodalbiometric systems", Pattern Recognition, Vol. 43, no. 5, pp. 1789–1800, May 2010.
Poh, N., "Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms", Information Forensics and Security, vol. 4, Issue: 4 p p. 849- 866 , 2009.
Al-Osaimi, F.R., " Spatially Optimized Data-Level Fusion of Texture and Shape for Face Recognition ", Image Processing, Vol. 21 , Issue. 2, p p. 859- 872, 2012.
Shubhangi Sapkal., " Data level fusion for multi biometric system using face and finger ", International journal of Advanced research of computer science and electronics engineering, Vol 1, No 2, 2012
Shi-Jinn Horng, Yuan-Hsin Chen, Ray-Shine Run, Rong-Jian Chen and Jui-Lin Lai " An Improved Score Level Fusion in Multimodal Biometric Systems ", Parallel and Distributed Computing, Applications and Technologies, p p. 239-246, 2009
Hube, J.P. “Methods for estimating biometric score level fusion", Biometrics: Theory Applications and Systems (BTAS), p p. 1- 6, 2010.
Hanaa S. Ali, Mahmoud I and Abdalla, “ Score-Level Fusion for Efficient Multimodal Person Identification using Face and Speech ", International Journal of Computer Science and Information Security, vol.9, issue.4, p p.48-53, 2013.
Jihyeon Jang, “Score-level fusion in multiple biometrics using non-linear classification",Control, Automation, Robotics and Vision,p p. 417-421, 2008.
Mehdi Parviz and M. Shahram Moin, “Boosting Approach for Score Level Fusion in Multimodal Biometrics Based on AUC Maximization", Journal of Information Hiding and Multimedia Signal Processing, vol.2, Issue.1, 2011.
Yi Wang , Jiankun Hu and Fengling Han, "Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields", Applied Mathematics and Computation, vol. 185, pp.823–833, 2007.
Debnath Bhattacharyya, Poulami Das,Samir Kumar Bandyopadhyay and Tai-hoon Kim, "IRIS Texture Analysis and Feature Extraction for Biometric Pattern Recognition", International Journal of Database Theory and Application, vol. 1, no. 1, pp. 53-60, December 2008.
J. Daugman, “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns,” International Journal of Computer Vision, vol. 45, no. 1, pp. 25-38, 2001.
G. P. Zhang, "Neural networks for classification: a survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 30, pp. 451-462, 2000.
J. C. Patra and R. N. Pal, "A functional link artificial neural network for adaptive channel equalization," Signal Processing, vol. 43, pp. 181- 195, 1995.
S. Dehuri and S.-B. Cho, "A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN," Neural Computing & Applications vol. 19, pp. 187-205, 2010.
S. Haykin, "Neural Networks: A Comprehensive Foundation. ," The Knowledge Engineering Review vol. 13, pp. 409-412, 1999.
D. Pham, et al., "The Bees Algorithm," Manufacturing Engineering Centre, Cardiff University, UK, 2005.
D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Elsevier Applied Soft Computing, vol. 8, pp. 687-697, 2007.
Chinese Academy of Sciences - Institute of Automation (CASIA), Database of 756 Gray scale Eye Images, http://www.cbsr.ia.ac.cn/IrisDatabase.htm,Version 1.0, 2005.
Poinsot, A., Yang, F., Palmprint and face score level fusion for contactless small sample biometric recognition and verification, (2010) International Review on Computers and Software (IRECOS), 5 (2), pp. 156-167.
Liao, H., A palm print recognition algorithm on geometric invariant theory, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2582-2585.
Anzar, S.M., Sathidevi, P.S., Fusion of biometric modalities using multi-normalization and genetic algorithm, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 2810-2818.
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