Comment 70 for bug 507511

I tried myself the opencv facedetect example. I used the python version and made a little python script that applied the tag "person" to every picture with a face. It writes directly into the database, it's not connected with digikam at all.
Two things about this detector:
- Increase the minimum size of a face. I changed it to a minimum of 100x100. That way it takes about 0.6 seconds per picture (my pictures are on average about 7 MPx).
- A lot of false possitives.

Due to the amount of false positives, I think it is worth implementing our own face detection method. However, as a first step, it would be really nice to have this one as a base so we can build from it.

About the opencv approach. It is a cascade classifier built with a boosting technique on a set of haar features. The problem is that the set of haar features is too little, so we'll need a way to specify Haar features and calculate them. To my knowledge, openCV's predefined functions only calculate the reduced set of features used by its classifier.
There are other techniques to detect faces, however this one seems the most appropriate.