Face recognition: A Sparse Representation-based Classification using Independent Component Analysis
In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a...
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| creator | Karimi, M. M. Soltanian-Zadeh, H. |
| description | In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively. |
| doi_str_mv | 10.1109/ISTEL.2012.6483165 |
| format | Conference Proceeding |
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M. ; Soltanian-Zadeh, H.</creator><creatorcontrib>Karimi, M. M. ; Soltanian-Zadeh, H.</creatorcontrib><description>In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. 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M.</creatorcontrib><creatorcontrib>Soltanian-Zadeh, H.</creatorcontrib><title>Face recognition: A Sparse Representation-based Classification using Independent Component Analysis</title><title>6th International Symposium on Telecommunications (IST)</title><addtitle>ISTEL</addtitle><description>In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively.</description><subject>Equations</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>ICA</subject><subject>Lighting</subject><subject>Principal component analysis</subject><subject>Robustness</subject><subject>Sparse Representation based Classification</subject><subject>Training</subject><isbn>1467320722</isbn><isbn>9781467320726</isbn><isbn>9781467320733</isbn><isbn>9781467320719</isbn><isbn>1467320714</isbn><isbn>1467320730</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UEtOwzAUNEJIQMkFYOMLJPhvh10VtRApEhIt68pxniuj1InisOjtaaFs5vf0ZjEIPVJSUErK53qzXTUFI5QVShhOlbxCWakNFUpzRjTn1-j-3zB2i7KUvgghp2dNFL1Dbm0d4AncsI9hDkN8wUu8Ge2UAH_AOEGCONvzIW9tgg5XvU0p-OB-Q_ydQtzjOnYwwgnijKvhMA7xrJbR9scU0gO68bZPkF14gT7Xq231ljfvr3W1bPJAtZxzJoVRRnYcpLbEUQ_OOO-FIbQFyYQirVGKg6ZKWF8yZlrWCkss956qTvMFevrrDQCwG6dwsNNxd9mF_wAabVfT</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Karimi, M. M.</creator><creator>Soltanian-Zadeh, H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201211</creationdate><title>Face recognition: A Sparse Representation-based Classification using Independent Component Analysis</title><author>Karimi, M. M. ; Soltanian-Zadeh, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-2548685d3e57a0c1fec8cff4801be52460b8663e7164af9228b2b4a0a3ff16d73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Equations</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>ICA</topic><topic>Lighting</topic><topic>Principal component analysis</topic><topic>Robustness</topic><topic>Sparse Representation based Classification</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Karimi, M. M.</creatorcontrib><creatorcontrib>Soltanian-Zadeh, H.</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>Karimi, M. M.</au><au>Soltanian-Zadeh, H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Face recognition: A Sparse Representation-based Classification using Independent Component Analysis</atitle><btitle>6th International Symposium on Telecommunications (IST)</btitle><stitle>ISTEL</stitle><date>2012-11</date><risdate>2012</risdate><spage>1170</spage><epage>1174</epage><pages>1170-1174</pages><isbn>1467320722</isbn><isbn>9781467320726</isbn><eisbn>9781467320733</eisbn><eisbn>9781467320719</eisbn><eisbn>1467320714</eisbn><eisbn>1467320730</eisbn><abstract>In this paper, we will describe a new method based on Sparse Representation-based Classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively.</abstract><pub>IEEE</pub><doi>10.1109/ISTEL.2012.6483165</doi><tpages>5</tpages></addata></record> |
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| subjects | Equations Face recognition Feature extraction ICA Lighting Principal component analysis Robustness Sparse Representation based Classification Training |
| title | Face recognition: A Sparse Representation-based Classification using Independent Component Analysis |
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