Localising facial features with matched filters

Localising facial features with matched filters

This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. T...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Choi, Kwang Nam, Cross, Andrew D. J., Hancock, Edwin R.
Format: Buchkapitel
Sprache:eng
Schlagworte:
Face Recognition
Facial Feature
Localisation Error
Matched Filter
Training Image
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 20
container_issue
container_start_page 11
container_title
container_volume
creator Choi, Kwang Nam
Cross, Andrew D. J.
Hancock, Edwin R.
description This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. The matched filters are extracted from training images using inverse Fourier analysis. We provide an experimental evaluation of the method on the University of Berne face data-base. Here we explore the most effective choice of training data so that the filters can be effectively applied when the facial pose varies. We also evaluate the effectiveness of the method when facial occlusion due to spectacles is present.
doi_str_mv 10.1007/BFb0015974
format Book Chapter
fullrecord <record><control><sourceid>springer</sourceid><recordid>TN_cdi_springer_books_10_1007_BFb0015974</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>springer_books_10_1007_BFb0015974</sourcerecordid><originalsourceid>FETCH-LOGICAL-s188t-be327819d702f2b8374c89bc7e659a467949ee0429bbd53e9b874c1627a881383</originalsourceid><addsrcrecordid>eNpFULlOxDAUNJdEWNLwBSlpwr7n5-O5hBULSJFooI7sxGEDgaA4iN8nHBLTTDGHRiPEGcIFAtj11TYAoHZW7YkT0goMK6n1vsjQIJZEyh2I3Fn-0aQxIA9FBgSyXDJ0LPKUnmEBSVRMmVhXY-OHPvVvT0Xnm94PRRf9_DHFVHz286549XOzi23R9cMcp3Qqjjo_pJj_8Uo8bq8fNrdldX9zt7msyoTMcxkiScvoWguyk4HJqoZdaGw02nllrFMuRlDShdBqii7w4kAjrWdGYlqJ89_e9D4t2-JUh3F8STVC_X1E_X8EfQEdXUlk</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>book_chapter</recordtype></control><display><type>book_chapter</type><title>Localising facial features with matched filters</title><source>Springer Books</source><creator>Choi, Kwang Nam ; Cross, Andrew D. J. ; Hancock, Edwin R.</creator><contributor>Bigün, Josef ; Chollet, Gérard ; Borgefors, Gunilla</contributor><creatorcontrib>Choi, Kwang Nam ; Cross, Andrew D. J. ; Hancock, Edwin R. ; Bigün, Josef ; Chollet, Gérard ; Borgefors, Gunilla</creatorcontrib><description>This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. The matched filters are extracted from training images using inverse Fourier analysis. We provide an experimental evaluation of the method on the University of Berne face data-base. Here we explore the most effective choice of training data so that the filters can be effectively applied when the facial pose varies. We also evaluate the effectiveness of the method when facial occlusion due to spectacles is present.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540626602</identifier><identifier>ISBN: 3540626603</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540684255</identifier><identifier>EISBN: 9783540684251</identifier><identifier>DOI: 10.1007/BFb0015974</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Face Recognition ; Facial Feature ; Localisation Error ; Matched Filter ; Training Image</subject><ispartof>Audio- and Video-based Biometric Person Authentication, 1997, p.11-20</ispartof><rights>Springer-Verlag Berlin Heidelberg 1997</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/BFb0015974$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/BFb0015974$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>775,776,780,789,27902,38232,41418,42487</link.rule.ids></links><search><contributor>Bigün, Josef</contributor><contributor>Chollet, Gérard</contributor><contributor>Borgefors, Gunilla</contributor><creatorcontrib>Choi, Kwang Nam</creatorcontrib><creatorcontrib>Cross, Andrew D. J.</creatorcontrib><creatorcontrib>Hancock, Edwin R.</creatorcontrib><title>Localising facial features with matched filters</title><title>Audio- and Video-based Biometric Person Authentication</title><description>This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. The matched filters are extracted from training images using inverse Fourier analysis. We provide an experimental evaluation of the method on the University of Berne face data-base. Here we explore the most effective choice of training data so that the filters can be effectively applied when the facial pose varies. We also evaluate the effectiveness of the method when facial occlusion due to spectacles is present.</description><subject>Face Recognition</subject><subject>Facial Feature</subject><subject>Localisation Error</subject><subject>Matched Filter</subject><subject>Training Image</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540626602</isbn><isbn>3540626603</isbn><isbn>3540684255</isbn><isbn>9783540684251</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>1997</creationdate><recordtype>book_chapter</recordtype><sourceid/><recordid>eNpFULlOxDAUNJdEWNLwBSlpwr7n5-O5hBULSJFooI7sxGEDgaA4iN8nHBLTTDGHRiPEGcIFAtj11TYAoHZW7YkT0goMK6n1vsjQIJZEyh2I3Fn-0aQxIA9FBgSyXDJ0LPKUnmEBSVRMmVhXY-OHPvVvT0Xnm94PRRf9_DHFVHz286549XOzi23R9cMcp3Qqjjo_pJj_8Uo8bq8fNrdldX9zt7msyoTMcxkiScvoWguyk4HJqoZdaGw02nllrFMuRlDShdBqii7w4kAjrWdGYlqJ89_e9D4t2-JUh3F8STVC_X1E_X8EfQEdXUlk</recordid><startdate>19970101</startdate><enddate>19970101</enddate><creator>Choi, Kwang Nam</creator><creator>Cross, Andrew D. J.</creator><creator>Hancock, Edwin R.</creator><general>Springer Berlin Heidelberg</general><scope/></search><sort><creationdate>19970101</creationdate><title>Localising facial features with matched filters</title><author>Choi, Kwang Nam ; Cross, Andrew D. J. ; Hancock, Edwin R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-s188t-be327819d702f2b8374c89bc7e659a467949ee0429bbd53e9b874c1627a881383</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Face Recognition</topic><topic>Facial Feature</topic><topic>Localisation Error</topic><topic>Matched Filter</topic><topic>Training Image</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Choi, Kwang Nam</creatorcontrib><creatorcontrib>Cross, Andrew D. J.</creatorcontrib><creatorcontrib>Hancock, Edwin R.</creatorcontrib></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Choi, Kwang Nam</au><au>Cross, Andrew D. J.</au><au>Hancock, Edwin R.</au><au>Bigün, Josef</au><au>Chollet, Gérard</au><au>Borgefors, Gunilla</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Localising facial features with matched filters</atitle><btitle>Audio- and Video-based Biometric Person Authentication</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>1997-01-01</date><risdate>1997</risdate><spage>11</spage><epage>20</epage><pages>11-20</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540626602</isbn><isbn>3540626603</isbn><eisbn>3540684255</eisbn><eisbn>9783540684251</eisbn><abstract>This paper describes a study of facial feature recognition using matched filter techniques. The basic aim is to develop a set of filters that can be used to characterise each of eight different facial features. These are left and right eyes, left and right-eyebrows, hairline, nose, mouth and chin. The matched filters are extracted from training images using inverse Fourier analysis. We provide an experimental evaluation of the method on the University of Berne face data-base. Here we explore the most effective choice of training data so that the filters can be effectively applied when the facial pose varies. We also evaluate the effectiveness of the method when facial occlusion due to spectacles is present.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/BFb0015974</doi><tpages>10</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0302-9743
ispartof Audio- and Video-based Biometric Person Authentication, 1997, p.11-20
issn 0302-9743
1611-3349
language eng
recordid cdi_springer_books_10_1007_BFb0015974
source Springer Books
subjects Face Recognition
Facial Feature
Localisation Error
Matched Filter
Training Image
title Localising facial features with matched filters
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T02%3A05%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-springer&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=bookitem&rft.atitle=Localising%20facial%20features%20with%20matched%20filters&rft.btitle=Audio-%20and%20Video-based%20Biometric%20Person%20Authentication&rft.au=Choi,%20Kwang%20Nam&rft.date=1997-01-01&rft.spage=11&rft.epage=20&rft.pages=11-20&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540626602&rft.isbn_list=3540626603&rft_id=info:doi/10.1007/BFb0015974&rft_dat=%3Cspringer%3Espringer_books_10_1007_BFb0015974%3C/springer%3E%3Curl%3E%3C/url%3E&rft.eisbn=3540684255&rft.eisbn_list=9783540684251&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true