Difference in face detection with default training sets

Recently I posted binaries and packaged libraries for face detection based on OpenCV an OpenIMAJ here and here. Basically both employ similar algorithms to detect faces in photos. As this is based on supervised classification not only the algorithm but also the employed training set has strong influence on the actual precision (and recall) of results. So out of interest I took a look on how well the results of both libraries are correlated:

imaj_20   1.000 0.933 0.695
imaj_40   0.933 1.000 0.706
opencv_   0.695 0.706 1.000

Above table shows the Pearson correlation of the face detection algorithm with the default models of OpenIMAJ (with a minimum face size of 20 and 40 pixels) and OpenCV. As can be seen the results correlate, but are not the same. Conclusion is: make sure that you check which one to use for your aplication and eventually train one yourself (as actually recommended by the documentation of both libraries).

This experiment has been done on just 171 images, but experiments with larger data sets have shown similar results.