Advanced Biometric Identification on Face, Gender and Age Recognition
The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results whe...
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| creator | Ramesha, K. Patnaik, L.M. Ramesha, K. Srikanth, N. Venugopal, K.R. |
| description | The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results when only small training set is available and it works even with a raining set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively. |
| doi_str_mv | 10.1109/ARTCom.2009.21 |
| format | Conference Proceeding |
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In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results when only small training set is available and it works even with a raining set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.</description><identifier>ISBN: 9781424451043</identifier><identifier>ISBN: 1424451043</identifier><identifier>EISBN: 0769538452</identifier><identifier>EISBN: 9780769538457</identifier><identifier>DOI: 10.1109/ARTCom.2009.21</identifier><language>eng</language><publisher>IEEE</publisher><subject>Age Classification ; Artificial neural networks ; Artificial NeuralNetworks ; Biometrics ; Eyes ; Face recognition ; Feature extraction ; Gender Classification ; Humans ; Image recognition ; Mouth ; Nose ; Shape ; Shape and Texture Transformation ; Wrinkle Texture</subject><ispartof>2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009, p.23-27</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5328100$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5328100$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ramesha, K.</creatorcontrib><creatorcontrib>Patnaik, L.M.</creatorcontrib><creatorcontrib>Ramesha, K.</creatorcontrib><creatorcontrib>Srikanth, N.</creatorcontrib><creatorcontrib>Venugopal, K.R.</creatorcontrib><title>Advanced Biometric Identification on Face, Gender and Age Recognition</title><title>2009 International Conference on Advances in Recent Technologies in Communication and Computing</title><addtitle>ARTCOM</addtitle><description>The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. 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It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.</description><subject>Age Classification</subject><subject>Artificial neural networks</subject><subject>Artificial NeuralNetworks</subject><subject>Biometrics</subject><subject>Eyes</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Gender Classification</subject><subject>Humans</subject><subject>Image recognition</subject><subject>Mouth</subject><subject>Nose</subject><subject>Shape</subject><subject>Shape and Texture Transformation</subject><subject>Wrinkle Texture</subject><isbn>9781424451043</isbn><isbn>1424451043</isbn><isbn>0769538452</isbn><isbn>9780769538457</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjE1LxDAURSMiqGO3btzkB9ial-8sa5kZBwaEYVwPafIyRGwrbRH891b0cuEs7uEScg-sAmDuqT4cm6GrOGOu4nBBbpnRTgkrFb8khTMWJJdSAZPimhTT9M6WLKNl-oas6_jl-4CRPuehw3nMge4i9nNOOfg5Dz1duvEBH-kW-4gj9X2k9RnpAcNw7vOvc0eukv-YsPjnirxt1sfmpdy_bndNvS8zGDWXDoXWivEY28hasFpbbkPiCQVYAOdsElwhWt96Dtwkn6LSwdgQpeCtESvy8PebEfH0OebOj98nJbgFxsQPmWVLCQ</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Ramesha, K.</creator><creator>Patnaik, L.M.</creator><creator>Ramesha, K.</creator><creator>Srikanth, N.</creator><creator>Venugopal, K.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>Advanced Biometric Identification on Face, Gender and Age Recognition</title><author>Ramesha, K. ; Patnaik, L.M. ; Ramesha, K. ; Srikanth, N. ; Venugopal, K.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-9e366502ddbd0b1866828cf2fe31811998f325ee8aba2127fafd56c78cd432b73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Age Classification</topic><topic>Artificial neural networks</topic><topic>Artificial NeuralNetworks</topic><topic>Biometrics</topic><topic>Eyes</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Gender Classification</topic><topic>Humans</topic><topic>Image recognition</topic><topic>Mouth</topic><topic>Nose</topic><topic>Shape</topic><topic>Shape and Texture Transformation</topic><topic>Wrinkle Texture</topic><toplevel>online_resources</toplevel><creatorcontrib>Ramesha, K.</creatorcontrib><creatorcontrib>Patnaik, L.M.</creatorcontrib><creatorcontrib>Ramesha, K.</creatorcontrib><creatorcontrib>Srikanth, N.</creatorcontrib><creatorcontrib>Venugopal, K.R.</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 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>Ramesha, K.</au><au>Patnaik, L.M.</au><au>Ramesha, K.</au><au>Srikanth, N.</au><au>Venugopal, K.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Advanced Biometric Identification on Face, Gender and Age Recognition</atitle><btitle>2009 International Conference on Advances in Recent Technologies in Communication and Computing</btitle><stitle>ARTCOM</stitle><date>2009-10</date><risdate>2009</risdate><spage>23</spage><epage>27</epage><pages>23-27</pages><isbn>9781424451043</isbn><isbn>1424451043</isbn><eisbn>0769538452</eisbn><eisbn>9780769538457</eisbn><abstract>The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR)algorithm for face recognition that yields good results when only small training set is available and it works even with a raining set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.</abstract><pub>IEEE</pub><doi>10.1109/ARTCom.2009.21</doi><tpages>5</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Age Classification Artificial neural networks Artificial NeuralNetworks Biometrics Eyes Face recognition Feature extraction Gender Classification Humans Image recognition Mouth Nose Shape Shape and Texture Transformation Wrinkle Texture |
| title | Advanced Biometric Identification on Face, Gender and Age Recognition |
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