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Discriminant Feature Manifold for Facial Aging Estimation

Hui Fang, Phil Grant, Min Chen, Philip Grant

Pattern Recognition (ICPR), 2010 20th International Conference on, Volume: 23-26 Aug. 2010, Pages: 593 - 596

Swansea University Author: Philip Grant

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DOI (Published version): 10.1109/ICPR.2010.150

Abstract

<p>Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace is proposed to extract more discriminating and...

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Published in: Pattern Recognition (ICPR), 2010 20th International Conference on
Published: 2010
URI: https://cronfa.swan.ac.uk/Record/cronfa5280
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2013-10-17T14:33:24.8097036</datestamp><bib-version>v2</bib-version><id>5280</id><entry>2012-02-23</entry><title>Discriminant Feature Manifold for Facial Aging Estimation</title><swanseaauthors><author><sid>3c75caf0df70504d841270d636835fde</sid><ORCID/><firstname>Philip</firstname><surname>Grant</surname><name>Philip Grant</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2012-02-23</date><deptcode>SCS</deptcode><abstract>&lt;p&gt;Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace is proposed to extract more discriminating and robust representations for aging estimation. After aligning all the faces by a piece-wise affine transform, orthogonal locality preserving projection (OLPP) is employed to project local binary patterns (LBP) from the faces into an age-discriminant subspace. The feature extracted from this manifold is more distinctive for age estimation compared with the features using in the state-of-the-art methods. Based on the public database FG-NET, the performance of the proposed feature is evaluated by using two different regression techniques, quadratic function and neural-network regression. The proposed feature subspace achieves the best performance based on both types of regression.&lt;/p&gt;</abstract><type>Journal Article</type><journal>Pattern Recognition (ICPR), 2010 20th International Conference on</journal><volume>23-26 Aug. 2010</volume><journalNumber></journalNumber><paginationStart>593</paginationStart><paginationEnd>596</paginationEnd><publisher/><placeOfPublication/><issnPrint/><issnElectronic/><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2010</publishedYear><publishedDate>2010-12-31</publishedDate><doi>10.1109/ICPR.2010.150</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2013-10-17T14:33:24.8097036</lastEdited><Created>2012-02-23T17:01:47.0000000</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Hui</firstname><surname>Fang</surname><order>1</order></author><author><firstname>Phil</firstname><surname>Grant</surname><order>2</order></author><author><firstname>Min</firstname><surname>Chen</surname><order>3</order></author><author><firstname>Philip</firstname><surname>Grant</surname><orcid/><order>4</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2013-10-17T14:33:24.8097036 v2 5280 2012-02-23 Discriminant Feature Manifold for Facial Aging Estimation 3c75caf0df70504d841270d636835fde Philip Grant Philip Grant true false 2012-02-23 SCS <p>Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace is proposed to extract more discriminating and robust representations for aging estimation. After aligning all the faces by a piece-wise affine transform, orthogonal locality preserving projection (OLPP) is employed to project local binary patterns (LBP) from the faces into an age-discriminant subspace. The feature extracted from this manifold is more distinctive for age estimation compared with the features using in the state-of-the-art methods. Based on the public database FG-NET, the performance of the proposed feature is evaluated by using two different regression techniques, quadratic function and neural-network regression. The proposed feature subspace achieves the best performance based on both types of regression.</p> Journal Article Pattern Recognition (ICPR), 2010 20th International Conference on 23-26 Aug. 2010 593 596 31 12 2010 2010-12-31 10.1109/ICPR.2010.150 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2013-10-17T14:33:24.8097036 2012-02-23T17:01:47.0000000 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Hui Fang 1 Phil Grant 2 Min Chen 3 Philip Grant 4
title Discriminant Feature Manifold for Facial Aging Estimation
spellingShingle Discriminant Feature Manifold for Facial Aging Estimation
Philip Grant
title_short Discriminant Feature Manifold for Facial Aging Estimation
title_full Discriminant Feature Manifold for Facial Aging Estimation
title_fullStr Discriminant Feature Manifold for Facial Aging Estimation
title_full_unstemmed Discriminant Feature Manifold for Facial Aging Estimation
title_sort Discriminant Feature Manifold for Facial Aging Estimation
author_id_str_mv 3c75caf0df70504d841270d636835fde
author_id_fullname_str_mv 3c75caf0df70504d841270d636835fde_***_Philip Grant
author Philip Grant
author2 Hui Fang
Phil Grant
Min Chen
Philip Grant
format Journal article
container_title Pattern Recognition (ICPR), 2010 20th International Conference on
container_volume 23-26 Aug. 2010
container_start_page 593
publishDate 2010
institution Swansea University
doi_str_mv 10.1109/ICPR.2010.150
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description <p>Computerised facial aging estimation, which has the potential for many applications in human-computer interactions, has been investigated by many computer vision researchers in recent years. In this paper, a feature-based discriminant subspace is proposed to extract more discriminating and robust representations for aging estimation. After aligning all the faces by a piece-wise affine transform, orthogonal locality preserving projection (OLPP) is employed to project local binary patterns (LBP) from the faces into an age-discriminant subspace. The feature extracted from this manifold is more distinctive for age estimation compared with the features using in the state-of-the-art methods. Based on the public database FG-NET, the performance of the proposed feature is evaluated by using two different regression techniques, quadratic function and neural-network regression. The proposed feature subspace achieves the best performance based on both types of regression.</p>
published_date 2010-12-31T03:06:19Z
_version_ 1763749690035142656
score 11.012678

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