Fusion of face and speech data for person identity verification
Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the perf...
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| Veröffentlicht in: | IEEE transactions on neural networks 1999, Vol.10 (5), p.1065-1074 |
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| container_title | IEEE transactions on neural networks |
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| creator | Ben-Yacoub, S. Abdeljaoued, Y. Mayoraz, E. |
| description | Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers. |
| doi_str_mv | 10.1109/72.788647 |
| format | Article |
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The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. 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The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers.</description><subject>Authentication</subject><subject>Bayesian analysis</subject><subject>Bayesian methods</subject><subject>Biometrics</subject><subject>Classification</subject><subject>Classification tree analysis</subject><subject>Classifiers</subject><subject>Decision trees</subject><subject>Fingerprint recognition</subject><subject>Multilayer perceptrons</subject><subject>Robustness</subject><subject>Speech</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><issn>1045-9227</issn><issn>1941-0093</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c9LwzAUB_AgipvTg1cPkpPioTMvSfPjJDKcCgMvei5Z-oqVra1NK-y_N9KiN3fKI-_zHiRfQs6BzQGYvdV8ro1RUh-QKVgJCWNWHMaayTSxnOsJOQnhgzGQKVPHZAKGp1wxOyV3yz6UdUXrghbOI3VVTkOD6N9p7jpHi7qlDbYhkjLHqiu7Hf3CtixK77o4eEqOCrcJeDaeM_K2fHhdPCWrl8fnxf0q8VKwLrFMGrUWa8iFEUoVwst4zxSgdVYZLSTk3uYehILUKJFL7biXpki9BhRWzMj1sLdp688eQ5dty-Bxs3EV1n3ILFgLXFu5V2oh4uNBpFFe_Su5EfEHld4PldVCWxPhzQB9W4fQYpE1bbl17S4Dlv1ElWmeDVFFezku7ddbzP_kmE0EFwMoEfG3PU5_Azpmkxw</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Ben-Yacoub, S.</creator><creator>Abdeljaoued, Y.</creator><creator>Mayoraz, E.</creator><general>IEEE</general><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>7SP</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>1999</creationdate><title>Fusion of face and speech data for person identity verification</title><author>Ben-Yacoub, S. ; Abdeljaoued, Y. ; Mayoraz, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-90486b3b1d38366f3c4430061e9a9687341dc9dc13615863d47a2c48f5c71e393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Authentication</topic><topic>Bayesian analysis</topic><topic>Bayesian methods</topic><topic>Biometrics</topic><topic>Classification</topic><topic>Classification tree analysis</topic><topic>Classifiers</topic><topic>Decision trees</topic><topic>Fingerprint recognition</topic><topic>Multilayer perceptrons</topic><topic>Robustness</topic><topic>Speech</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Ben-Yacoub, S.</creatorcontrib><creatorcontrib>Abdeljaoued, Y.</creatorcontrib><creatorcontrib>Mayoraz, E.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ben-Yacoub, S.</au><au>Abdeljaoued, Y.</au><au>Mayoraz, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fusion of face and speech data for person identity verification</atitle><jtitle>IEEE transactions on neural networks</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>1999</date><risdate>1999</risdate><volume>10</volume><issue>5</issue><spage>1065</spage><epage>1074</epage><pages>1065-1074</pages><issn>1045-9227</issn><eissn>1941-0093</eissn><coden>ITNNEP</coden><abstract>Biometric person identity authentication is gaining more and more attention. 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| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transactions on neural networks, 1999, Vol.10 (5), p.1065-1074 |
| issn | 1045-9227 1941-0093 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Authentication Bayesian analysis Bayesian methods Biometrics Classification Classification tree analysis Classifiers Decision trees Fingerprint recognition Multilayer perceptrons Robustness Speech Support vector machine classification Support vector machines |
| title | Fusion of face and speech data for person identity verification |
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