Abstract
In this paper we present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihood detection of faces. Excellent results are reported in terms of the correct-answer-false-alarm tradeoff as well as in terms of the computational requirements of the systems.
| Original language | English (US) |
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| Pages | 307-311 |
| Number of pages | 5 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition - Killington, VT, USA Duration: Oct 14 1996 → Oct 16 1996 |
Other
| Other | Proceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition |
|---|---|
| City | Killington, VT, USA |
| Period | 10/14/96 → 10/16/96 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition