Feature Selection for Face Detection
We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion...
| Main Authors: | , , , |
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7232 |
| _version_ | 1826210045802577920 |
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| author | Serre, Thomas Heisele, Bernd Mukherjee, Sayan Poggio, Tomaso |
| author_facet | Serre, Thomas Heisele, Bernd Mukherjee, Sayan Poggio, Tomaso |
| author_sort | Serre, Thomas |
| collection | MIT |
| description | We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM. |
| first_indexed | 2024-09-23T14:40:43Z |
| id | mit-1721.1/7232 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:40:43Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72322019-04-10T11:52:47Z Feature Selection for Face Detection Serre, Thomas Heisele, Bernd Mukherjee, Sayan Poggio, Tomaso We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM. 2004-10-20T21:03:34Z 2004-10-20T21:03:34Z 2000-09-01 AIM-1697 CBCL-192 http://hdl.handle.net/1721.1/7232 en_US AIM-1697 CBCL-192 7211022 bytes 1034240 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Serre, Thomas Heisele, Bernd Mukherjee, Sayan Poggio, Tomaso Feature Selection for Face Detection |
| title | Feature Selection for Face Detection |
| title_full | Feature Selection for Face Detection |
| title_fullStr | Feature Selection for Face Detection |
| title_full_unstemmed | Feature Selection for Face Detection |
| title_short | Feature Selection for Face Detection |
| title_sort | feature selection for face detection |
| url | http://hdl.handle.net/1721.1/7232 |
| work_keys_str_mv | AT serrethomas featureselectionforfacedetection AT heiselebernd featureselectionforfacedetection AT mukherjeesayan featureselectionforfacedetection AT poggiotomaso featureselectionforfacedetection |