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I can confirm that cascade classifiers are for from useful in this case, since the tend to model rigid objects, unless you are using a template combination of multiple cascade classifiers all responding to a specific orientation.

More frequently used setups for hand detection:

1. Color based segmentation. The most basic accepted rule for skin segmentation is the RmG rule --> 20 < R - G < 80
2. There is the skin classifier based on Naive bayes classifier, which is built on the dbskin dataset. This could be trained in OpenCV also.
3. Finally the Piotr Dollar ICF/ACF framework showed that tranferring the image to the LUV color space allows for easy segmentation in the U channel.
4. There is also a deformable hand model available using the DPM framework cited by Mittal.

I can confirm that cascade classifiers are for far from useful in this case, since the tend to model rigid objects, unless you are using a template combination of multiple cascade classifiers all responding to a specific orientation.

More frequently used setups for hand detection:

1. Color based segmentation. The most basic accepted rule for skin segmentation is the RmG rule --> 20 < R - G < 80
2. There is the skin classifier based on Naive bayes classifier, which is built on the dbskin dataset. This could be trained in OpenCV also.
3. Finally the Piotr Dollar ICF/ACF framework showed that tranferring the image to the LUV color space allows for easy segmentation in the U channel.
4. There is also a deformable hand model available using the DPM framework cited by Mittal.