Abstract
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 "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector measurements, whether or not a human face exists at the current image location. We show empirically that the distance metric we adopt for computing difference feature vectors, and the "nonface" clusters we include in our distribution-based model, are both critical for the success of our system.
Keywords
Artificial intelligencePattern recognition (psychology)Computer scienceFeature vectorFacial recognition systemClassifier (UML)Face (sociological concept)Face detectionFeature (linguistics)Computer visionMetric (unit)
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Publication Info
- Year
- 1998
- Type
- article
- Volume
- 20
- Issue
- 1
- Pages
- 39-51
- Citations
- 1767
- Access
- Closed
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Cite This
Kah Kay Sung,
Tomaso Poggio
(1998).
Example-based learning for view-based human face detection.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 20
(1)
, 39-51.
https://doi.org/10.1109/34.655648
Identifiers
- DOI
- 10.1109/34.655648