Application of Multi-weighted Neuron for Iris Recognition
In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the ori...
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| creator | Cao, Wenming Hu, Jianhui Xiao, Gang Wang, Shoujue |
| description | In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective. |
| doi_str_mv | 10.1007/11427445_15 |
| format | Conference Proceeding |
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In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540259138</identifier><identifier>ISBN: 3540259139</identifier><identifier>ISBN: 9783540259121</identifier><identifier>ISBN: 3540259120</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540320679</identifier><identifier>EISBN: 9783540320678</identifier><identifier>DOI: 10.1007/11427445_15</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Biometric Technology ; Computer science; control theory; systems ; Exact sciences and technology ; Iris Image ; Iris Recognition ; Iris Sample ; Learning and adaptive systems ; Weighted Neuron</subject><ispartof>Advances in Neural Networks – ISNN 2005, 2005, p.87-92</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11427445_15$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11427445_15$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,776,777,781,786,787,790,4036,4037,27906,38236,41423,42492</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16882780$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Yi, Zhang</contributor><contributor>Liao, Xiao-Feng</contributor><contributor>Wang, Jun</contributor><creatorcontrib>Cao, Wenming</creatorcontrib><creatorcontrib>Hu, Jianhui</creatorcontrib><creatorcontrib>Xiao, Gang</creatorcontrib><creatorcontrib>Wang, Shoujue</creatorcontrib><title>Application of Multi-weighted Neuron for Iris Recognition</title><title>Advances in Neural Networks – ISNN 2005</title><description>In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biometric Technology</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Iris Image</subject><subject>Iris Recognition</subject><subject>Iris Sample</subject><subject>Learning and adaptive systems</subject><subject>Weighted Neuron</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540259138</isbn><isbn>3540259139</isbn><isbn>9783540259121</isbn><isbn>3540259120</isbn><isbn>3540320679</isbn><isbn>9783540320678</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkLtOxDAQRc1LIiyp-IE0FBQBj-34Ua5WPFZaQEJQW44dB0NIIjsrxN-T1VIwzR3pnJniInQB-BowFjcAjAjGKg3VATqjFcOUYC7UIcqAA5SUMnWEciXkjpFKAZXHKMMUk1IJRk9RntIHnocCn28zpJbj2AVrpjD0xeCLx203hfK7Ce371LjiqdnGGfghFusYUvHS2KHtw84-RyfedKnJ_3KB3u5uX1cP5eb5fr1absqRgJpKxoSQzjtMCHU1tdZ461ntlFRGEMspMa52jiuQEioBvjaVZ4AFd3zeJV2gy_3f0SRrOh9Nb0PSYwxfJv5o4FISIfHsXe29NKO-baKuh-EzacB6153-1x39Bc2yW0M</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Cao, Wenming</creator><creator>Hu, Jianhui</creator><creator>Xiao, Gang</creator><creator>Wang, Shoujue</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Application of Multi-weighted Neuron for Iris Recognition</title><author>Cao, Wenming ; Hu, Jianhui ; Xiao, Gang ; Wang, Shoujue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-44778dfd0223db3ccafcf4bd989a72c632adbdd691881571fba5f41076d6fba83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Biometric Technology</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Iris Image</topic><topic>Iris Recognition</topic><topic>Iris Sample</topic><topic>Learning and adaptive systems</topic><topic>Weighted Neuron</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cao, Wenming</creatorcontrib><creatorcontrib>Hu, Jianhui</creatorcontrib><creatorcontrib>Xiao, Gang</creatorcontrib><creatorcontrib>Wang, Shoujue</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cao, Wenming</au><au>Hu, Jianhui</au><au>Xiao, Gang</au><au>Wang, Shoujue</au><au>Yi, Zhang</au><au>Liao, Xiao-Feng</au><au>Wang, Jun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application of Multi-weighted Neuron for Iris Recognition</atitle><btitle>Advances in Neural Networks – ISNN 2005</btitle><date>2005</date><risdate>2005</risdate><spage>87</spage><epage>92</epage><pages>87-92</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540259138</isbn><isbn>3540259139</isbn><isbn>9783540259121</isbn><isbn>3540259120</isbn><eisbn>3540320679</eisbn><eisbn>9783540320678</eisbn><abstract>In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as “cognition” one class by one class, and it doesn’t influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11427445_15</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Neural Networks – ISNN 2005, 2005, p.87-92 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16882780 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Biometric Technology Computer science control theory systems Exact sciences and technology Iris Image Iris Recognition Iris Sample Learning and adaptive systems Weighted Neuron |
| title | Application of Multi-weighted Neuron for Iris Recognition |
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