Neuro-fuzzy inference system based face recognition using feature extraction
Artificial neural networks (ANN) were used widely for constructing intelligent computer systems based on image processing and pattern recognition [4]. The proposed system consist of two stages: first stage face recognition by using NN and second stage is to evaluate the performance of the proposed a...
Gespeichert in:
| Veröffentlicht in: | Telkomnika 2020-02, Vol.18 (1), p.427-435 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 435 |
|---|---|
| container_issue | 1 |
| container_start_page | 427 |
| container_title | Telkomnika |
| container_volume | 18 |
| creator | Abdullah, Hamsa A. |
| description | Artificial neural networks (ANN) were used widely for constructing intelligent computer systems based on image processing and pattern recognition [4]. The proposed system consist of two stages: first stage face recognition by using NN and second stage is to evaluate the performance of the proposed algorithm with fuzzy system. 2.FACE RECOGNITION TECHNIQUES The main steps to face recognition are; extracting the features from the images, store features in data base, design NN, train feature on network, and test the old and new data NN. 2.1. In FL the idea of fractional truth has been used, where the range could be fully false and fully true. [...]a specific functions may be used to manage linguistic variable. In the first stage, the set of images are training to supply the data to network. [...]the designing structure of input required the identical row from the image matrix as shown in Figure 2. |
| doi_str_mv | 10.12928/telkomnika.v18i1.12992 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2379529429</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2379529429</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1952-2202d4a96b5abba13b85580ee6416d906acf8b5ce669feaf94efa38ae5e71bc43</originalsourceid><addsrcrecordid>eNpFkFtLAzEQhYMoWGp_gwGft-ayyW4epXiDoi_6HJJ0UtLLpia7YvvrTVvBgWHgzDCH8yF0S8mUMsXa-x4267jtwtpMv2kb6FFW7AKNGCesUkzxSzSiUvGqNLlGk5xXpFRDmFDtCM3fYEix8sPhsMeh85Cgc4DzPvewxdZkWGBvipLAxWUX-hA7POTQLbEH0w8JMPz0ybjj4gZdebPJMPmbY_T59Pgxe6nm78-vs4d55agSrGKMsEVtlLTCWGsot60QLQGQNZULRaRxvrXCgZSqmHhVgze8NSCgodbVfIzuzn93KX4NkHu9ikPqiqVmvCkWqi7Bx6g5X7kUc07g9S6FrUl7TYk-0dP_9PSJnj7R47-I5GiV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2379529429</pqid></control><display><type>article</type><title>Neuro-fuzzy inference system based face recognition using feature extraction</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Abdullah, Hamsa A.</creator><creatorcontrib>Abdullah, Hamsa A.</creatorcontrib><description>Artificial neural networks (ANN) were used widely for constructing intelligent computer systems based on image processing and pattern recognition [4]. The proposed system consist of two stages: first stage face recognition by using NN and second stage is to evaluate the performance of the proposed algorithm with fuzzy system. 2.FACE RECOGNITION TECHNIQUES The main steps to face recognition are; extracting the features from the images, store features in data base, design NN, train feature on network, and test the old and new data NN. 2.1. In FL the idea of fractional truth has been used, where the range could be fully false and fully true. [...]a specific functions may be used to manage linguistic variable. In the first stage, the set of images are training to supply the data to network. [...]the designing structure of input required the identical row from the image matrix as shown in Figure 2.</description><identifier>ISSN: 1693-6930</identifier><identifier>EISSN: 2302-9293</identifier><identifier>DOI: 10.12928/telkomnika.v18i1.12992</identifier><language>eng</language><publisher>Yogyakarta: Ahmad Dahlan University</publisher><subject>Algorithms ; Artificial neural networks ; Back propagation ; Face recognition ; Feature extraction ; Feature recognition ; Fuzzy logic ; Fuzzy systems ; Image processing ; Methods ; Neural networks ; Object recognition ; Pattern recognition ; Principal components analysis ; Standard deviation</subject><ispartof>Telkomnika, 2020-02, Vol.18 (1), p.427-435</ispartof><rights>2020. This work is published under https://creativecommons.org/licenses/by/3.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Abdullah, Hamsa A.</creatorcontrib><title>Neuro-fuzzy inference system based face recognition using feature extraction</title><title>Telkomnika</title><description>Artificial neural networks (ANN) were used widely for constructing intelligent computer systems based on image processing and pattern recognition [4]. The proposed system consist of two stages: first stage face recognition by using NN and second stage is to evaluate the performance of the proposed algorithm with fuzzy system. 2.FACE RECOGNITION TECHNIQUES The main steps to face recognition are; extracting the features from the images, store features in data base, design NN, train feature on network, and test the old and new data NN. 2.1. In FL the idea of fractional truth has been used, where the range could be fully false and fully true. [...]a specific functions may be used to manage linguistic variable. In the first stage, the set of images are training to supply the data to network. [...]the designing structure of input required the identical row from the image matrix as shown in Figure 2.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Feature recognition</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Image processing</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Pattern recognition</subject><subject>Principal components analysis</subject><subject>Standard deviation</subject><issn>1693-6930</issn><issn>2302-9293</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpFkFtLAzEQhYMoWGp_gwGft-ayyW4epXiDoi_6HJJ0UtLLpia7YvvrTVvBgWHgzDCH8yF0S8mUMsXa-x4267jtwtpMv2kb6FFW7AKNGCesUkzxSzSiUvGqNLlGk5xXpFRDmFDtCM3fYEix8sPhsMeh85Cgc4DzPvewxdZkWGBvipLAxWUX-hA7POTQLbEH0w8JMPz0ybjj4gZdebPJMPmbY_T59Pgxe6nm78-vs4d55agSrGKMsEVtlLTCWGsot60QLQGQNZULRaRxvrXCgZSqmHhVgze8NSCgodbVfIzuzn93KX4NkHu9ikPqiqVmvCkWqi7Bx6g5X7kUc07g9S6FrUl7TYk-0dP_9PSJnj7R47-I5GiV</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Abdullah, Hamsa A.</creator><general>Ahmad Dahlan University</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200201</creationdate><title>Neuro-fuzzy inference system based face recognition using feature extraction</title><author>Abdullah, Hamsa A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1952-2202d4a96b5abba13b85580ee6416d906acf8b5ce669feaf94efa38ae5e71bc43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Back propagation</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Feature recognition</topic><topic>Fuzzy logic</topic><topic>Fuzzy systems</topic><topic>Image processing</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Pattern recognition</topic><topic>Principal components analysis</topic><topic>Standard deviation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abdullah, Hamsa A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East & South Asia Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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><jtitle>Telkomnika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdullah, Hamsa A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neuro-fuzzy inference system based face recognition using feature extraction</atitle><jtitle>Telkomnika</jtitle><date>2020-02-01</date><risdate>2020</risdate><volume>18</volume><issue>1</issue><spage>427</spage><epage>435</epage><pages>427-435</pages><issn>1693-6930</issn><eissn>2302-9293</eissn><abstract>Artificial neural networks (ANN) were used widely for constructing intelligent computer systems based on image processing and pattern recognition [4]. The proposed system consist of two stages: first stage face recognition by using NN and second stage is to evaluate the performance of the proposed algorithm with fuzzy system. 2.FACE RECOGNITION TECHNIQUES The main steps to face recognition are; extracting the features from the images, store features in data base, design NN, train feature on network, and test the old and new data NN. 2.1. In FL the idea of fractional truth has been used, where the range could be fully false and fully true. [...]a specific functions may be used to manage linguistic variable. In the first stage, the set of images are training to supply the data to network. [...]the designing structure of input required the identical row from the image matrix as shown in Figure 2.</abstract><cop>Yogyakarta</cop><pub>Ahmad Dahlan University</pub><doi>10.12928/telkomnika.v18i1.12992</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1693-6930 |
| ispartof | Telkomnika, 2020-02, Vol.18 (1), p.427-435 |
| issn | 1693-6930 2302-9293 |
| language | eng |
| recordid | cdi_proquest_journals_2379529429 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithms Artificial neural networks Back propagation Face recognition Feature extraction Feature recognition Fuzzy logic Fuzzy systems Image processing Methods Neural networks Object recognition Pattern recognition Principal components analysis Standard deviation |
| title | Neuro-fuzzy inference system based face recognition using feature extraction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T01%3A30%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Neuro-fuzzy%20inference%20system%20based%20face%20recognition%20using%20feature%20extraction&rft.jtitle=Telkomnika&rft.au=Abdullah,%20Hamsa%20A.&rft.date=2020-02-01&rft.volume=18&rft.issue=1&rft.spage=427&rft.epage=435&rft.pages=427-435&rft.issn=1693-6930&rft.eissn=2302-9293&rft_id=info:doi/10.12928/telkomnika.v18i1.12992&rft_dat=%3Cproquest_cross%3E2379529429%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2379529429&rft_id=info:pmid/&rfr_iscdi=true |