I want to implement an on-the-fly training process for face detection. I'm not sure but it seems it's called online boosting.
Вefore, I have used OpenCV face detection with my own cascades, but I'm interested in technique which can "learn in process".
For example, If I train a boost classifer with 1000 marked images and then I look at the results on my test set, if I'm satisfied, the algorithm will finish. Otherwise, I will add 100 images more but then not retrain all 1100 images, just add 100 images to existing cascade, is it possible? It's also usefull for training in which can participate many people or computers.
What if I have big in-class difference, for example, frontal faces and rotated faces. Do I need to train 2 separate classifiers?