Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique
Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition i...
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Gespeichert in:
| Autor*in: |
Karmakar, Dhiman [verfasserIn] Datta, Madhura [verfasserIn] Murthy, C.A [verfasserIn] |
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Englisch |
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2013 |
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Enthalten in: International journal of software science and computational intelligence - Hershey, Pa : IGI Global, 2009, 5(2013), 3, Seite 22-32 |
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volume:5 ; year:2013 ; number:3 ; pages:22-32 |
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10.4018/ijssci.2013070102 |
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10.4018/ijssci.2013070102 doi (DE-627)NLEJ251833313 (VZGNL)10.4018/ijssci.2013070102 DE-627 ger DE-627 rakwb eng Karmakar, Dhiman verfasserin aut Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique Convex Combination Equal Error Rate (EER) False Acceptance Rate (FAR) False Rejection Rate (FRR) Feature Extraction and Reduction Feature Level Fusion Intra-Class Threshold Minimal Spanning Tree (MST) Multimodal Biometrics Set Estimation Datta, Madhura verfasserin aut Murthy, C.A verfasserin aut Enthalten in International journal of software science and computational intelligence Hershey, Pa : IGI Global, 2009 5(2013), 3, Seite 22-32 Online-Ressource (DE-627)NLEJ244419531 (DE-600)2703774-5 1942-9037 nnns volume:5 year:2013 number:3 pages:22-32 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2013 3 22-32 |
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10.4018/ijssci.2013070102 doi (DE-627)NLEJ251833313 (VZGNL)10.4018/ijssci.2013070102 DE-627 ger DE-627 rakwb eng Karmakar, Dhiman verfasserin aut Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique Convex Combination Equal Error Rate (EER) False Acceptance Rate (FAR) False Rejection Rate (FRR) Feature Extraction and Reduction Feature Level Fusion Intra-Class Threshold Minimal Spanning Tree (MST) Multimodal Biometrics Set Estimation Datta, Madhura verfasserin aut Murthy, C.A verfasserin aut Enthalten in International journal of software science and computational intelligence Hershey, Pa : IGI Global, 2009 5(2013), 3, Seite 22-32 Online-Ressource (DE-627)NLEJ244419531 (DE-600)2703774-5 1942-9037 nnns volume:5 year:2013 number:3 pages:22-32 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2013 3 22-32 |
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10.4018/ijssci.2013070102 doi (DE-627)NLEJ251833313 (VZGNL)10.4018/ijssci.2013070102 DE-627 ger DE-627 rakwb eng Karmakar, Dhiman verfasserin aut Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique 2013 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique Convex Combination Equal Error Rate (EER) False Acceptance Rate (FAR) False Rejection Rate (FRR) Feature Extraction and Reduction Feature Level Fusion Intra-Class Threshold Minimal Spanning Tree (MST) Multimodal Biometrics Set Estimation Datta, Madhura verfasserin aut Murthy, C.A verfasserin aut Enthalten in International journal of software science and computational intelligence Hershey, Pa : IGI Global, 2009 5(2013), 3, Seite 22-32 Online-Ressource (DE-627)NLEJ244419531 (DE-600)2703774-5 1942-9037 nnns volume:5 year:2013 number:3 pages:22-32 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssci.2013070102&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2013 3 22-32 |
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Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique |
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Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251833313</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205144018.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijssci.2013070102</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251833313</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijssci.2013070102</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Karmakar, Dhiman</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. 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