Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the g...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2008-03, Vol.30 (3), p.541-547 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Makinen, E. Raisamo, R. |
| description | We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the maximum classification accuracy. |
| doi_str_mv | 10.1109/TPAMI.2007.70800 |
| format | Article |
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We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the maximum classification accuracy.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>DOI: 10.1109/TPAMI.2007.70800</identifier><identifier>PMID: 18195447</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Algorithms ; Alignment ; Application software ; Applied sciences ; Artificial Intelligence ; Biometry - methods ; Classification ; Classifier design and evaluation ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Computer vision ; Data processing. List processing. Character string processing ; Detectors ; Exact sciences and technology ; Eyes ; Face - anatomy & histology ; Face - physiology ; Face and gesture recognition ; Face detection ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Information Storage and Retrieval - methods ; Intelligence ; Interactive systems ; Machine learning ; Memory organisation. Data processing ; Neural networks ; Pattern analysis ; Pattern Recognition, Automated - methods ; Pattern recognition. Digital image processing. 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We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the maximum classification accuracy.</description><subject>Algorithms</subject><subject>Alignment</subject><subject>Application software</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Biometry - methods</subject><subject>Classification</subject><subject>Classifier design and evaluation</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Computer vision</subject><subject>Data processing. List processing. Character string processing</subject><subject>Detectors</subject><subject>Exact sciences and technology</subject><subject>Eyes</subject><subject>Face - anatomy & histology</subject><subject>Face - physiology</subject><subject>Face and gesture recognition</subject><subject>Face detection</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Intelligence</subject><subject>Interactive systems</subject><subject>Machine learning</subject><subject>Memory organisation. Data processing</subject><subject>Neural networks</subject><subject>Pattern analysis</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Sex Characteristics</subject><subject>Sex Determination Analysis - methods</subject><subject>Sex Factors</subject><subject>Software</subject><subject>Subtraction Technique</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Vision I/O</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkctP3DAQh62KCrbAvRISiiq1PWU7fsSP42oLFAlED_TQk-XYEzDKJhAnrfjv632ISj2Uky3PN5_G8yPkPYU5pWC-3H5fXF_OGYCaK9AAb8iMGm5KXnGzR2ZAJSu1ZvqAvEvpAYCKCvg-OaCamkoINSM_z365dnJj7Luib4oL7AIOxbJ1KcUm-m3hGsf7PqTidxzvi8U09qv87l3bPhdfcUQ_YihcF4pFG--6fD93HtMRedu4NuHx7jwkP87Pbpffyqubi8vl4qr0QpqxlNIAhyC9kiCDD8rVWmHtkWIdlPeN05lgFARUBipqAkNELlG7Gpgy_JB83nofh_5pwjTaVUwe29Z12E_JZr0UXDH5KqlVBULwDfnpv6QCBoZK_irIsy9bIYMf_gEf-mno8mKsloxXTIDKEGwhP_QpDdjYxyGu3PBsKdh14HYTuF0HbjeB55bTnXeqVxj-NuwSzsDHHeBSjqwZXOdjeuGyqsoDrr98suViXu9LOU-vuVb8DwnbukY</recordid><startdate>20080301</startdate><enddate>20080301</enddate><creator>Makinen, E.</creator><creator>Raisamo, R.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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User interface</topic><topic>Computer vision</topic><topic>Data processing. List processing. Character string processing</topic><topic>Detectors</topic><topic>Exact sciences and technology</topic><topic>Eyes</topic><topic>Face - anatomy & histology</topic><topic>Face - physiology</topic><topic>Face and gesture recognition</topic><topic>Face detection</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Intelligence</topic><topic>Interactive systems</topic><topic>Machine learning</topic><topic>Memory organisation. Data processing</topic><topic>Neural networks</topic><topic>Pattern analysis</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Pattern recognition. Digital image processing. 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We experimented with 120 combinations of automatic face detection, face alignment, and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the maximum classification accuracy.</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>18195447</pmid><doi>10.1109/TPAMI.2007.70800</doi><tpages>7</tpages></addata></record> |
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| subjects | Algorithms Alignment Application software Applied sciences Artificial Intelligence Biometry - methods Classification Classifier design and evaluation Computer science control theory systems Computer systems and distributed systems. User interface Computer vision Data processing. List processing. Character string processing Detectors Exact sciences and technology Eyes Face - anatomy & histology Face - physiology Face and gesture recognition Face detection Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Intelligence Interactive systems Machine learning Memory organisation. Data processing Neural networks Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reproducibility of Results Sensitivity and Specificity Sex Characteristics Sex Determination Analysis - methods Sex Factors Software Subtraction Technique Support vector machine classification Support vector machines Vision I/O |
| title | Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces |
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