Human Augmented Cognition Based on Integration of Visual and Auditory Information
Identifieur interne : 004B68 ( Istex/Corpus ); précédent : 004B67; suivant : 004B69Human Augmented Cognition Based on Integration of Visual and Auditory Information
Auteurs : Jae Won ; Wono Lee ; Sang-Woo Ban ; Minook Kim ; Hyung-Min Park ; Minho LeeSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.
Url:
DOI: 10.1007/978-3-642-15246-7_50
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wolee@ee.knu.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Ban, Sang Woo" sort="Ban, Sang Woo" uniqKey="Ban S" first="Sang-Woo" last="Ban">Sang-Woo Ban</name><affiliation><mods:affiliation>Department of Information & Communication Engineering, Dongguk University, 707 Seokjang-Dong, Gyeongju, 780-714, Gyeongbuk, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: swban@dongguk.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Kim, Minook" sort="Kim, Minook" uniqKey="Kim M" first="Minook" last="Kim">Minook Kim</name><affiliation><mods:affiliation>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: min8328@sogang.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Park, Hyung Min" sort="Park, Hyung Min" uniqKey="Park H" first="Hyung-Min" last="Park">Hyung-Min 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Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: wolee@ee.knu.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Ban, Sang Woo" sort="Ban, Sang Woo" uniqKey="Ban S" first="Sang-Woo" last="Ban">Sang-Woo Ban</name><affiliation><mods:affiliation>Department of Information & Communication Engineering, Dongguk University, 707 Seokjang-Dong, Gyeongju, 780-714, Gyeongbuk, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: swban@dongguk.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Kim, Minook" sort="Kim, Minook" uniqKey="Kim M" first="Minook" last="Kim">Minook Kim</name><affiliation><mods:affiliation>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: min8328@sogang.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Park, Hyung Min" sort="Park, Hyung Min" uniqKey="Park H" first="Hyung-Min" last="Park">Hyung-Min Park</name><affiliation><mods:affiliation>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: hpark@sogang.ac.kr</mods:affiliation></affiliation></author><author><name sortKey="Lee, Minho" sort="Lee, Minho" uniqKey="Lee M" first="Minho" last="Lee">Minho Lee</name><affiliation><mods:affiliation>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: mholee@knu.ac.kr</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2010</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno 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In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Woong Jae Won</name><affiliations><json:string>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</json:string><json:string>E-mail: wwj@ee.knu.ac.kr</json:string></affiliations></json:item><json:item><name>Wono Lee</name><affiliations><json:string>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</json:string><json:string>E-mail: wolee@ee.knu.ac.kr</json:string></affiliations></json:item><json:item><name>Sang-Woo Ban</name><affiliations><json:string>Department of Information & Communication Engineering, Dongguk University, 707 Seokjang-Dong, Gyeongju, 780-714, Gyeongbuk, Korea</json:string><json:string>E-mail: swban@dongguk.ac.kr</json:string></affiliations></json:item><json:item><name>Minook Kim</name><affiliations><json:string>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</json:string><json:string>E-mail: min8328@sogang.ac.kr</json:string></affiliations></json:item><json:item><name>Hyung-Min Park</name><affiliations><json:string>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</json:string><json:string>E-mail: hpark@sogang.ac.kr</json:string></affiliations></json:item><json:item><name>Minho Lee</name><affiliations><json:string>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</json:string><json:string>E-mail: mholee@knu.ac.kr</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.</abstract><qualityIndicators><score>6.457</score><pdfVersion>1.3</pdfVersion><pdfPageSize>430 x 660 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>1271</abstractCharCount><pdfWordCount>4285</pdfWordCount><pdfCharCount>25817</pdfCharCount><pdfPageCount>11</pdfPageCount><abstractWordCount>181</abstractWordCount></qualityIndicators><title>Human Augmented Cognition Based on Integration of Visual and Auditory Information</title><genre.original><json:string>OriginalPaper</json:string></genre.original><chapterId><json:string>50</json:string><json:string>Chap50</json:string></chapterId><genre><json:string>conference [eBooks]</json:string></genre><serie><issn><json:string>0302-9743</json:string></issn><author><json:item><name>David Hutchison</name><affiliations><json:string>Lancaster University, Lancaster, UK</json:string></affiliations></json:item><json:item><name>Takeo Kanade</name><affiliations><json:string>Carnegie Mellon University, Pittsburgh, PA, USA</json:string></affiliations></json:item><json:item><name>Josef Kittler</name><affiliations><json:string>University of Surrey, Guildford, UK</json:string></affiliations></json:item><json:item><name>Jon M. 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Computer Science, Saarbrücken, Germany</affiliation></editor><biblScope><date>2010</date></biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesId">558</idno></series><series><title level="s">Lecture Notes in Artificial Intelligence</title><editor><persName><forename type="first">Randy</forename><surname>Goebel</surname></persName><affiliation>University of Alberta, Edmonton, Canada</affiliation></editor><editor><persName><forename type="first">Jörg</forename><surname>Siekmann</surname></persName><affiliation>University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Wolfgang</forename><surname>Wahlster</surname></persName><affiliation>DFKI and University of Saarland, Saarbrücken, Germany</affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><biblScope type="seriesId">1244</biblScope></series><idno type="istex">CCDFD3F3AE4C31DFC95EEE441D4754D601241AB9</idno><idno type="DOI">10.1007/978-3-642-15246-7_50</idno><idno type="ChapterID">50</idno><idno type="ChapterID">Chap50</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2010</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I21017</label><label>I18040</label><label>I18024</label><label>I18032</label><label>I16013</label><label>I18030</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. 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DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Weikum</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>Lancaster University</OrgName><OrgAddress><City>Lancaster</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford 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Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><AuthorGroup><Author AffiliationIDS="Aff16"><AuthorName DisplayOrder="Western"><GivenName>Randy</GivenName><FamilyName>Goebel</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff17"><AuthorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>Wolfgang</GivenName><FamilyName>Wahlster</FamilyName></AuthorName></Author><Affiliation ID="Aff16"><OrgName>University of Alberta</OrgName><OrgAddress><City>Edmonton</City><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>DFKI and University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup></SubSeriesHeader></SubSeries><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" 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Proceedings</BookSubTitle><BookVolumeNumber>6230</BookVolumeNumber><BookSequenceNumber>6230</BookSequenceNumber><BookDOI>10.1007/978-3-642-15246-7</BookDOI><BookTitleID>213144</BookTitleID><BookPrintISBN>978-3-642-15245-0</BookPrintISBN><BookElectronicISBN>978-3-642-15246-7</BookElectronicISBN><BookChapterCount>73</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2010</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I21017" Priority="1" Type="Secondary">Artificial Intelligence (incl. 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DisplayOrder="Western"><GivenName>Mehmet</GivenName><GivenName>A.</GivenName><FamilyName>Orgun</FamilyName></EditorName><Contact><Email>mehmet.orgun@mq.edu.au</Email></Contact></Editor><Affiliation ID="Aff19"><OrgDivision>School of Computer Science and Engineering</OrgDivision><OrgName>Seoul National University</OrgName><OrgAddress><Postcode>151-744</Postcode><City>Seoul</City><Country>Korea</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Department of Computing,</OrgDivision><OrgName>Macquarie University</OrgName><OrgAddress><Postcode>NSW</Postcode><City>Sydney</City><Country>Australia</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part3"><PartInfo TocLevels="0"><PartID>3</PartID><PartSequenceNumber>3</PartSequenceNumber><PartTitle>Special Session Papers</PartTitle><PartChapterCount>16</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>PRICAI 2010: Trends in Artificial Intelligence</BookTitle></PartContext></PartInfo><Chapter ID="Chap50" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>50</ChapterID><ChapterDOI>10.1007/978-3-642-15246-7_50</ChapterDOI><ChapterSequenceNumber>50</ChapterSequenceNumber><ChapterTitle Language="En">Human Augmented Cognition Based on Integration of Visual and Auditory Information</ChapterTitle><ChapterFirstPage>547</ChapterFirstPage><ChapterLastPage>557</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2010</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>3</PartID><BookID>978-3-642-15246-7</BookID><BookTitle>PRICAI 2010: Trends in Artificial Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Woong</GivenName><GivenName>Jae</GivenName><FamilyName>Won</FamilyName></AuthorName><Contact><Email>wwj@ee.knu.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Wono</GivenName><FamilyName>Lee</FamilyName></AuthorName><Contact><Email>wolee@ee.knu.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff22"><AuthorName DisplayOrder="Western"><GivenName>Sang-Woo</GivenName><FamilyName>Ban</FamilyName></AuthorName><Contact><Email>swban@dongguk.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Minook</GivenName><FamilyName>Kim</FamilyName></AuthorName><Contact><Email>min8328@sogang.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Hyung-Min</GivenName><FamilyName>Park</FamilyName></AuthorName><Contact><Email>hpark@sogang.ac.kr</Email></Contact></Author><Author AffiliationIDS="Aff21"><AuthorName DisplayOrder="Western"><GivenName>Minho</GivenName><FamilyName>Lee</FamilyName></AuthorName><Contact><Email>mholee@knu.ac.kr</Email></Contact></Author><Affiliation ID="Aff21"><OrgDivision>School of Electrical Engineering and Computer Science</OrgDivision><OrgName>Kyungpook National University</OrgName><OrgAddress><Street>1370 Sankyuk-Dong, Puk-Gu</Street><City>Taegu</City><Postcode>702-701</Postcode><Country>Korea</Country></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgDivision>Department of Information & Communication Engineering</OrgDivision><OrgName>Dongguk University</OrgName><OrgAddress><Street>707 Seokjang-Dong, Gyeongju</Street><City>Gyeongbuk</City><Postcode>780-714</Postcode><Country>Korea</Country></OrgAddress></Affiliation><Affiliation ID="Aff23"><OrgDivision>Department of Electronic Engineering</OrgDivision><OrgName>Sogang University</OrgName><OrgAddress><Street>1 Shinsu-Dong, Mapo-Gu</Street><City>Seoul</City><Postcode>121-742</Postcode><Country>Korea</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Human Augmented Cognition Based on Integration of Visual and Auditory Information</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Human Augmented Cognition Based on Integration of Visual and Auditory Information</title></titleInfo><name type="personal"><namePart type="given">Woong</namePart><namePart type="given">Jae</namePart><namePart type="family">Won</namePart><affiliation>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</affiliation><affiliation>E-mail: wwj@ee.knu.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wono</namePart><namePart type="family">Lee</namePart><affiliation>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</affiliation><affiliation>E-mail: wolee@ee.knu.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sang-Woo</namePart><namePart type="family">Ban</namePart><affiliation>Department of Information & Communication Engineering, Dongguk University, 707 Seokjang-Dong, Gyeongju, 780-714, Gyeongbuk, Korea</affiliation><affiliation>E-mail: swban@dongguk.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Minook</namePart><namePart type="family">Kim</namePart><affiliation>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</affiliation><affiliation>E-mail: min8328@sogang.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Hyung-Min</namePart><namePart type="family">Park</namePart><affiliation>Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, 121-742, Seoul, Korea</affiliation><affiliation>E-mail: hpark@sogang.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Minho</namePart><namePart type="family">Lee</namePart><affiliation>School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea</affiliation><affiliation>E-mail: mholee@knu.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2010</dateIssued><copyrightDate encoding="w3cdtf">2010</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: In this paper, we propose a new multiple sensory fused human identification model for providing human augmented cognition. In the proposed model, both facial features and mel-frequency cepstral coefficients (MFCCs) are considered as visual features and auditory features for identifying a human, respectively. As well, an adaboosting model identifies a human using the integrated sensory features of both visual and auditory features. In the proposed model, facial form features are obtained from the principal component analysis (PCA) of a human’s face area localized by an Adaboost algorithm in conjunction with a skin color preferable attention model. Moreover, MFCCs are extracted from human speech. Thus, the proposed multiple sensory integration model is aimed to enhance the performance of human identification by considering both visual and auditory complementarily working under partly distorted sensory environments. A human augmented cognition system with the proposed human identification model is implemented as a goggle type, on which it presents information such as unknown people’s profile based on human identification. Experimental results show that the proposed model can plausibly conduct human identification in an indoor meeting situation.</abstract><relatedItem type="host"><titleInfo><title>PRICAI 2010: Trends in Artificial Intelligence</title><subTitle>11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30–September 2, 2010. 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Germany</affiliation></name><name type="personal"><namePart type="given">Wolfgang</namePart><namePart type="family">Wahlster</namePart><affiliation>DFKI and University of Saarland, Saarbrücken, Germany</affiliation></name><genre type="Sub-Series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2010</recordOrigin></recordInfo></relatedItem><identifier type="istex">CCDFD3F3AE4C31DFC95EEE441D4754D601241AB9</identifier><identifier type="DOI">10.1007/978-3-642-15246-7_50</identifier><identifier type="ChapterID">50</identifier><identifier type="ChapterID">Chap50</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 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