Example Based Learning for View-Based Human Face Detection
We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image...
| Main Authors: | , |
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/7193 |
| _version_ | 1826205270072623104 |
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| author | Sung, Kah Kay Poggio, Tomaso |
| author_facet | Sung, Kah Kay Poggio, Tomaso |
| author_sort | Sung, Kah Kay |
| collection | MIT |
| description | We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system. |
| first_indexed | 2024-09-23T13:10:01Z |
| id | mit-1721.1/7193 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:10:01Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/71932019-04-10T11:52:41Z Example Based Learning for View-Based Human Face Detection Sung, Kah Kay Poggio, Tomaso Face Detection Pattern Recognition Pattern Classification Learning from examples Object Recognition We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system. 2004-10-20T20:49:22Z 2004-10-20T20:49:22Z 1995-01-24 AIM-1521 CBCL-112 http://hdl.handle.net/1721.1/7193 en_US AIM-1521 CBCL-112 21 p. 2933946 bytes 846344 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Face Detection Pattern Recognition Pattern Classification Learning from examples Object Recognition Sung, Kah Kay Poggio, Tomaso Example Based Learning for View-Based Human Face Detection |
| title | Example Based Learning for View-Based Human Face Detection |
| title_full | Example Based Learning for View-Based Human Face Detection |
| title_fullStr | Example Based Learning for View-Based Human Face Detection |
| title_full_unstemmed | Example Based Learning for View-Based Human Face Detection |
| title_short | Example Based Learning for View-Based Human Face Detection |
| title_sort | example based learning for view based human face detection |
| topic | Face Detection Pattern Recognition Pattern Classification Learning from examples Object Recognition |
| url | http://hdl.handle.net/1721.1/7193 |
| work_keys_str_mv | AT sungkahkay examplebasedlearningforviewbasedhumanfacedetection AT poggiotomaso examplebasedlearningforviewbasedhumanfacedetection |