A kernel Gabor-based weighted region covariance matrix for face recognition

This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2012-06, Vol.12 (6), p.7410-7422
Main Authors: Qin, Huafeng, Qin, Lan, Xue, Lian, Li, Yantao
Format: Article
Language:English
Subjects:
Algorithms
Covariance matrix
Face recognition
Feature recognition
Gabor features
kernalization
Kernels
Preserves
Recognition
Sensors
weighted region covariance matrix
Weighting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3
cites cdi_FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3
container_end_page 7422
container_issue 6
container_start_page 7410
container_title Sensors (Basel, Switzerland)
container_volume 12
creator Qin, Huafeng
Qin, Lan
Xue, Lian
Li, Yantao
description This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).
doi_str_mv 10.3390/s120607410
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_f1c0ec81125a4adf8f30f5bb26f18f40</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_f1c0ec81125a4adf8f30f5bb26f18f40</doaj_id><sourcerecordid>3340613641</sourcerecordid><originalsourceid>FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3</originalsourceid><addsrcrecordid>eNqFkk1v1DAQhiMEoqVw4QegSFwQUmDssb3xBamqoFRU4gJnyx_j1Es2Lna2hX9Pli2l5cLJ4_GjRzPW2zTPGbxB1PC2Mg4KVoLBg-aQCS66nnN4eKc-aJ7UugbgiNg_bg4410qjZIfNp-P2G5WJxvbUulw6ZyuF9prScDEvRaEh5an1-cqWZCdP7cbOJf1oYy5ttMu9kM_DlOYFe9o8inas9OzmPGq-fnj_5eRjd_759Ozk-LzzEuTcOR2jYtKRViR7ACYCoPQRSJLnTrOgohIQGIjgvIMYNEayQqAHHXjEo-Zs7w3Zrs1lSRtbfppsk_ndyGUwtszJj2Qi80C-Z4xLK2yIfUSI0jmuIuujgMX1bu-63LoNBU_TXOx4T3r_ZUoXZshXBgVK3e8Er24EJX_fUp3NJlVP42gnyttqGHLkApSS_0cBNQrFQS_oy3_Qdd6WaflVwySu1GpZaCd8vad8ybUWirdzMzC7aJi_0VjgF3c3vUX_ZAF_Ae7Xszw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1537671255</pqid></control><display><type>article</type><title>A kernel Gabor-based weighted region covariance matrix for face recognition</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Qin, Huafeng ; Qin, Lan ; Xue, Lian ; Li, Yantao</creator><creatorcontrib>Qin, Huafeng ; Qin, Lan ; Xue, Lian ; Li, Yantao</creatorcontrib><description>This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s120607410</identifier><identifier>PMID: 22969351</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Algorithms ; Covariance matrix ; Face recognition ; Feature recognition ; Gabor features ; kernalization ; Kernels ; Preserves ; Recognition ; Sensors ; weighted region covariance matrix ; Weighting</subject><ispartof>Sensors (Basel, Switzerland), 2012-06, Vol.12 (6), p.7410-7422</ispartof><rights>Copyright MDPI AG 2012</rights><rights>2012 by the authors; licensee MDPI, Basel, Switzerland. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3</citedby><cites>FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1537671255/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1537671255?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22969351$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Qin, Huafeng</creatorcontrib><creatorcontrib>Qin, Lan</creatorcontrib><creatorcontrib>Xue, Lian</creatorcontrib><creatorcontrib>Li, Yantao</creatorcontrib><title>A kernel Gabor-based weighted region covariance matrix for face recognition</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).</description><subject>Algorithms</subject><subject>Covariance matrix</subject><subject>Face recognition</subject><subject>Feature recognition</subject><subject>Gabor features</subject><subject>kernalization</subject><subject>Kernels</subject><subject>Preserves</subject><subject>Recognition</subject><subject>Sensors</subject><subject>weighted region covariance matrix</subject><subject>Weighting</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqFkk1v1DAQhiMEoqVw4QegSFwQUmDssb3xBamqoFRU4gJnyx_j1Es2Lna2hX9Pli2l5cLJ4_GjRzPW2zTPGbxB1PC2Mg4KVoLBg-aQCS66nnN4eKc-aJ7UugbgiNg_bg4410qjZIfNp-P2G5WJxvbUulw6ZyuF9prScDEvRaEh5an1-cqWZCdP7cbOJf1oYy5ttMu9kM_DlOYFe9o8inas9OzmPGq-fnj_5eRjd_759Ozk-LzzEuTcOR2jYtKRViR7ACYCoPQRSJLnTrOgohIQGIjgvIMYNEayQqAHHXjEo-Zs7w3Zrs1lSRtbfppsk_ndyGUwtszJj2Qi80C-Z4xLK2yIfUSI0jmuIuujgMX1bu-63LoNBU_TXOx4T3r_ZUoXZshXBgVK3e8Er24EJX_fUp3NJlVP42gnyttqGHLkApSS_0cBNQrFQS_oy3_Qdd6WaflVwySu1GpZaCd8vad8ybUWirdzMzC7aJi_0VjgF3c3vUX_ZAF_Ae7Xszw</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Qin, Huafeng</creator><creator>Qin, Lan</creator><creator>Xue, Lian</creator><creator>Li, Yantao</creator><general>MDPI AG</general><general>Molecular Diversity Preservation International (MDPI)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20120601</creationdate><title>A kernel Gabor-based weighted region covariance matrix for face recognition</title><author>Qin, Huafeng ; Qin, Lan ; Xue, Lian ; Li, Yantao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Covariance matrix</topic><topic>Face recognition</topic><topic>Feature recognition</topic><topic>Gabor features</topic><topic>kernalization</topic><topic>Kernels</topic><topic>Preserves</topic><topic>Recognition</topic><topic>Sensors</topic><topic>weighted region covariance matrix</topic><topic>Weighting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qin, Huafeng</creatorcontrib><creatorcontrib>Qin, Lan</creatorcontrib><creatorcontrib>Xue, Lian</creatorcontrib><creatorcontrib>Li, Yantao</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qin, Huafeng</au><au>Qin, Lan</au><au>Xue, Lian</au><au>Li, Yantao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A kernel Gabor-based weighted region covariance matrix for face recognition</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><addtitle>Sensors (Basel)</addtitle><date>2012-06-01</date><risdate>2012</risdate><volume>12</volume><issue>6</issue><spage>7410</spage><epage>7422</epage><pages>7410-7422</pages><issn>1424-8220</issn><eissn>1424-8220</eissn><abstract>This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM).</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>22969351</pmid><doi>10.3390/s120607410</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1424-8220
ispartof Sensors (Basel, Switzerland), 2012-06, Vol.12 (6), p.7410-7422
issn 1424-8220
1424-8220
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_f1c0ec81125a4adf8f30f5bb26f18f40
source Publicly Available Content Database; PubMed Central
subjects Algorithms
Covariance matrix
Face recognition
Feature recognition
Gabor features
kernalization
Kernels
Preserves
Recognition
Sensors
weighted region covariance matrix
Weighting
title A kernel Gabor-based weighted region covariance matrix for face recognition
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A25%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20kernel%20Gabor-based%20weighted%20region%20covariance%20matrix%20for%20face%20recognition&rft.jtitle=Sensors%20(Basel,%20Switzerland)&rft.au=Qin,%20Huafeng&rft.date=2012-06-01&rft.volume=12&rft.issue=6&rft.spage=7410&rft.epage=7422&rft.pages=7410-7422&rft.issn=1424-8220&rft.eissn=1424-8220&rft_id=info:doi/10.3390/s120607410&rft_dat=%3Cproquest_doaj_%3E3340613641%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c505t-b9ff615be96e580014d035cf0e5ec2b91d6f640d104dbcb0fd93fea443c09d2f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1537671255&rft_id=info:pmid/22969351&rfr_iscdi=true