GA-Neural Approach for Latent Finger Print Matching
Latent finger print matching is one of the freshest areas in science. The current methods of latent finger print matching are manual and reliable on human experience. Unfortunately, a system, which can perform the latent fingerprint matching automatically, does not exist. The eye tracking technology...
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| creator | Shapoori, Shahrzad Allinson, Nigel |
| description | Latent finger print matching is one of the freshest areas in science. The current methods of latent finger print matching are manual and reliable on human experience. Unfortunately, a system, which can perform the latent fingerprint matching automatically, does not exist. The eye tracking technology is able to record the eye movement and could provide useful information about the user search strategy. In this paper, the experimental data obtained from an eye tracker is analyzed by clustering analysis and a neural network based system is designed to learn the search strategy of the experts. The results show that the system is able to predict the optimum search strategy based on expert's experiences. |
| doi_str_mv | 10.1109/ISMS.2011.19 |
| format | Conference Proceeding |
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The results show that the system is able to predict the optimum search strategy based on expert's experiences.</description><subject>Artificial neural networks</subject><subject>Clustering algorithms</subject><subject>eye tracker</subject><subject>finger print identification</subject><subject>Fingerprint recognition</subject><subject>Fingers</subject><subject>Gallium</subject><subject>genetic algorithm</subject><subject>Humans</subject><subject>latent finger print</subject><subject>neural network</subject><subject>Tracking</subject><issn>2166-0662</issn><issn>2166-0670</issn><isbn>1424498090</isbn><isbn>9781424498093</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9TE1PwkAU3KgkAnLz5mX_QOt7b7f7cWyIIEkBE7iT7XYrNQjNth7895ZonMNM5iPD2CNCigj2ebVb71ICxBTtDRsTKpWA0nDLJihJSmvAwt1_oWjEJte5vRLes1nXfcCAjLSRcszEMk824Su6E8_bNl6cP_L6Ennh-nDu-aI5v4fI32IzmLXr_XEIHtiodqcuzP50yvaLl_38NSm2y9U8L5LGQp-gV6Sk0xpsDWgzgqomD0KZCmXlfemhNNphcDozplaikiEzKAhcaQyRmLKn39smhHBoY_Pp4vch0wIEWvEDi-BFrA</recordid><startdate>201101</startdate><enddate>201101</enddate><creator>Shapoori, Shahrzad</creator><creator>Allinson, Nigel</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201101</creationdate><title>GA-Neural Approach for Latent Finger Print Matching</title><author>Shapoori, Shahrzad ; Allinson, Nigel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1c6264a7709f019520df2c0368d14dccbc0b87a1ea7588f63d4e581320ab88223</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Clustering algorithms</topic><topic>eye tracker</topic><topic>finger print identification</topic><topic>Fingerprint recognition</topic><topic>Fingers</topic><topic>Gallium</topic><topic>genetic algorithm</topic><topic>Humans</topic><topic>latent finger print</topic><topic>neural network</topic><topic>Tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Shapoori, Shahrzad</creatorcontrib><creatorcontrib>Allinson, Nigel</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shapoori, Shahrzad</au><au>Allinson, Nigel</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>GA-Neural Approach for Latent Finger Print Matching</atitle><btitle>2011 Second International Conference on Intelligent Systems, Modelling and Simulation</btitle><stitle>isms</stitle><date>2011-01</date><risdate>2011</risdate><spage>49</spage><epage>52</epage><pages>49-52</pages><issn>2166-0662</issn><eissn>2166-0670</eissn><isbn>1424498090</isbn><isbn>9781424498093</isbn><abstract>Latent finger print matching is one of the freshest areas in science. The current methods of latent finger print matching are manual and reliable on human experience. Unfortunately, a system, which can perform the latent fingerprint matching automatically, does not exist. The eye tracking technology is able to record the eye movement and could provide useful information about the user search strategy. In this paper, the experimental data obtained from an eye tracker is analyzed by clustering analysis and a neural network based system is designed to learn the search strategy of the experts. The results show that the system is able to predict the optimum search strategy based on expert's experiences.</abstract><pub>IEEE</pub><doi>10.1109/ISMS.2011.19</doi><tpages>4</tpages></addata></record> |
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| subjects | Artificial neural networks Clustering algorithms eye tracker finger print identification Fingerprint recognition Fingers Gallium genetic algorithm Humans latent finger print neural network Tracking |
| title | GA-Neural Approach for Latent Finger Print Matching |
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