A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification
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 b...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2013/515918 |
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| author | Ujwalla Gawande Mukesh Zaveri Avichal Kapur |
| author_facet | Ujwalla Gawande Mukesh Zaveri Avichal Kapur |
| author_sort | Ujwalla Gawande |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d097f2edad4348229cc304e628e0e1c5 |
| institution | DOAJ |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-d097f2edad4348229cc304e628e0e1c52025-08-20T03:19:37ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322013-01-01201310.1155/2013/515918515918A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person IdentificationUjwalla Gawande0Mukesh Zaveri1Avichal Kapur2Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur 441110, IndiaDepartment of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, IndiaNagar Yuwak Shikshan Sanstha, Nagpur, IndiaRecent 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.http://dx.doi.org/10.1155/2013/515918 |
| spellingShingle | Ujwalla Gawande Mukesh Zaveri Avichal Kapur A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification Applied Computational Intelligence and Soft Computing |
| title | A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification |
| title_full | A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification |
| title_fullStr | A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification |
| title_full_unstemmed | A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification |
| title_short | A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification |
| title_sort | novel algorithm for feature level fusion using svm classifier for multibiometrics based person identification |
| url | http://dx.doi.org/10.1155/2013/515918 |
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