Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2007-03, Vol.18 (2), p.506-519 |
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| creator | Kyperountas, M. Tefas, A. Pitas, I. |
| description | A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance |
| doi_str_mv | 10.1109/TNN.2006.885038 |
| format | Article |
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The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. 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(IEEE) 2007</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-3bb2449c8cab211f3fb9664de2c67955c8e5608790b8316470c625e3ce6c3f513</citedby><cites>FETCH-LOGICAL-c440t-3bb2449c8cab211f3fb9664de2c67955c8e5608790b8316470c625e3ce6c3f513</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4118267$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4118267$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17385635$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kyperountas, M.</creatorcontrib><creatorcontrib>Tefas, A.</creatorcontrib><creatorcontrib>Pitas, I.</creatorcontrib><title>Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biometrics</subject><subject>Biometry - methods</subject><subject>Classification</subject><subject>Computer Simulation</subject><subject>Discriminant Analysis</subject><subject>Face</subject><subject>Face - anatomy & histology</subject><subject>Face verification</subject><subject>Humans</subject><subject>Hyperplanes</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Informatics</subject><subject>Information security</subject><subject>Information Storage and Retrieval - methods</subject><subject>Linear discriminant analysis</subject><subject>linear discriminant analysis (LDA)</subject><subject>Linear Models</subject><subject>Neural networks</subject><subject>Pattern analysis</subject><subject>Pattern recognition</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Phases</subject><subject>Sample Size</subject><subject>Similarity</subject><subject>small sample size (SSS) problem</subject><subject>Studies</subject><subject>System testing</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNp9kU1rFEEQhhtRTIyePQjSeNDTbKr6a7qPIRoVlhjYRI_NTG9N0mE-Nt2zCfrr7bCLggdPVUU9VfDyMPYaYYEI7vjy_HwhAMzCWg3SPmGH6BRWAE4-LT0oXTkh6gP2IudbAFQazHN2gLW02kh9yK5-ULy-mWnNLyIFeoiZ-PLjCe-mxFdTfx_Haz7fEF8NTd_zVTNs-jLEX8Qv0tT2NPA48rMmEP9OKXYxNHOcxpfsWdf0mV7t6xG7Ovt0efqlWn77_PX0ZFkFpWCuZNsKpVywoWkFYie71hmj1iSCqZ3WwZI2YGsHrZVoVA3BCE0ykAmy0yiP2Ifd302a7raUZz_EHKjvm5GmbfbWgikxhSrk-_-SNUhwCKKA7_4Bb6dtGksK71AIVEKYAh3voJCmnBN1fpPi0KSfHsE_ivFFjH8U43diysXb_dttO9D6L783UYA3OyAS0Z-1QrTC1PI3OBiOtw</recordid><startdate>20070301</startdate><enddate>20070301</enddate><creator>Kyperountas, M.</creator><creator>Tefas, A.</creator><creator>Pitas, I.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20070301</creationdate><title>Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification</title><author>Kyperountas, M. ; Tefas, A. ; Pitas, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-3bb2449c8cab211f3fb9664de2c67955c8e5608790b8316470c625e3ce6c3f513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biometrics</topic><topic>Biometry - methods</topic><topic>Classification</topic><topic>Computer Simulation</topic><topic>Discriminant Analysis</topic><topic>Face</topic><topic>Face - anatomy & histology</topic><topic>Face verification</topic><topic>Humans</topic><topic>Hyperplanes</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Informatics</topic><topic>Information security</topic><topic>Information Storage and Retrieval - methods</topic><topic>Linear discriminant analysis</topic><topic>linear discriminant analysis (LDA)</topic><topic>Linear Models</topic><topic>Neural networks</topic><topic>Pattern analysis</topic><topic>Pattern recognition</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Phases</topic><topic>Sample Size</topic><topic>Similarity</topic><topic>small sample size (SSS) problem</topic><topic>Studies</topic><topic>System testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Kyperountas, M.</creatorcontrib><creatorcontrib>Tefas, A.</creatorcontrib><creatorcontrib>Pitas, I.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kyperountas, M.</au><au>Tefas, A.</au><au>Pitas, I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2007-03-01</date><risdate>2007</risdate><volume>18</volume><issue>2</issue><spage>506</spage><epage>519</epage><pages>506-519</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance</abstract><cop>United States</cop><pub>IEEE</pub><pmid>17385635</pmid><doi>10.1109/TNN.2006.885038</doi><tpages>14</tpages></addata></record> |
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| subjects | Algorithms Artificial Intelligence Biometrics Biometry - methods Classification Computer Simulation Discriminant Analysis Face Face - anatomy & histology Face verification Humans Hyperplanes Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Informatics Information security Information Storage and Retrieval - methods Linear discriminant analysis linear discriminant analysis (LDA) Linear Models Neural networks Pattern analysis Pattern recognition Pattern Recognition, Automated - methods Phases Sample Size Similarity small sample size (SSS) problem Studies System testing |
| title | Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification |
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