Face Recognition Based on the Phase Spectrum of Local Normalized Image
This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination,...
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| creator | Olivares-Mercado, J. Hotta, K. Takahashi, H. Perez-Meana, H. Sanchez-Perez, G. |
| description | This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109 subjects (60 males and 49 females) under illumination changes, expression changes and partial occlusion. The proposed method provides results with a correct recognition rate more than 95.5%. |
| doi_str_mv | 10.1109/MICAI.2008.46 |
| format | Conference Proceeding |
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The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109 subjects (60 males and 49 females) under illumination changes, expression changes and partial occlusion. 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The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109 subjects (60 males and 49 females) under illumination changes, expression changes and partial occlusion. The proposed method provides results with a correct recognition rate more than 95.5%.</description><subject>Biometrics</subject><subject>Character recognition</subject><subject>Data mining</subject><subject>Face recognition</subject><subject>Image recognition</subject><subject>Lighting</subject><subject>Local Normalized Image</subject><subject>PCA</subject><subject>Phase Spectrum</subject><subject>Principal component analysis</subject><subject>Robustness</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>SVM</subject><isbn>0769534414</isbn><isbn>9780769534411</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotTslOwzAUtIQqQUuPnLj4BxK8vCzvCBWBSGERy7mynZfWKKmrJBzg6zGCucyi0WgYu5AilVLg1UO9ua5TJUSZQn7ClqLIMdMAEhZs-RujBkRxytbT9CEiNOpCyTNWVcYRfyEXdgc_-3DgN2ailkcx74k_76Pjr0dy8_g58NDxJjjT88cwDqb337FZD2ZH52zRmX6i9T-v2Ht1-7a5T5qnu3itSbwssjkhIHCkSouQF5IADXS2RVQOC6W6HMF1paQ2HjbWSmGMtq0AqyHLQOhSr9jl364nou1x9IMZv7aQlwoyrX8AnbBKCQ</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Olivares-Mercado, J.</creator><creator>Hotta, K.</creator><creator>Takahashi, H.</creator><creator>Perez-Meana, H.</creator><creator>Sanchez-Perez, G.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200810</creationdate><title>Face Recognition Based on the Phase Spectrum of Local Normalized Image</title><author>Olivares-Mercado, J. ; Hotta, K. ; Takahashi, H. ; Perez-Meana, H. ; Sanchez-Perez, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e4e4ce28b94671e49a4fbd992c9722f694cf81ed008abb10aa3bd04b345540383</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Biometrics</topic><topic>Character recognition</topic><topic>Data mining</topic><topic>Face recognition</topic><topic>Image recognition</topic><topic>Lighting</topic><topic>Local Normalized Image</topic><topic>PCA</topic><topic>Phase Spectrum</topic><topic>Principal component analysis</topic><topic>Robustness</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>SVM</topic><toplevel>online_resources</toplevel><creatorcontrib>Olivares-Mercado, J.</creatorcontrib><creatorcontrib>Hotta, K.</creatorcontrib><creatorcontrib>Takahashi, H.</creatorcontrib><creatorcontrib>Perez-Meana, H.</creatorcontrib><creatorcontrib>Sanchez-Perez, G.</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>Olivares-Mercado, J.</au><au>Hotta, K.</au><au>Takahashi, H.</au><au>Perez-Meana, H.</au><au>Sanchez-Perez, G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Face Recognition Based on the Phase Spectrum of Local Normalized Image</atitle><btitle>2008 Seventh Mexican International Conference on Artificial Intelligence</btitle><stitle>MICAI</stitle><date>2008-10</date><risdate>2008</risdate><spage>123</spage><epage>127</epage><pages>123-127</pages><isbn>0769534414</isbn><isbn>9780769534411</isbn><abstract>This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109 subjects (60 males and 49 females) under illumination changes, expression changes and partial occlusion. The proposed method provides results with a correct recognition rate more than 95.5%.</abstract><pub>IEEE</pub><doi>10.1109/MICAI.2008.46</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biometrics Character recognition Data mining Face recognition Image recognition Lighting Local Normalized Image PCA Phase Spectrum Principal component analysis Robustness Support vector machine classification Support vector machines SVM |
| title | Face Recognition Based on the Phase Spectrum of Local Normalized Image |
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