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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7193 |
| Date | 24 January 1995 |
| Creators | Sung, Kah Kay, Poggio, Tomaso |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 21 p., 2933946 bytes, 846344 bytes, application/postscript, application/pdf |
| Relation | AIM-1521, CBCL-112 |
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