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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 515918, 11 pages
Research Article

A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
2Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
3Nagar Yuwak Shikshan Sanstha, Nagpur, India

Received 1 April 2013; Revised 16 June 2013; Accepted 17 June 2013

Academic Editor: Zhang Yi

Copyright © 2013 Ujwalla Gawande et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.