This is not necessarily based on his code, but the way I believe Viola-Jones Adaptive Boosting works is as such: 1. Run the adaptive boosting code which create

This is not necessarily based on his code, but the way I believe Viola-Jones Adaptive Boosting works is as such: 1. Run the adaptive boosting code which creates a complex decision function using known positive and negative data. The code creates what is called an Integral Image from each piece of data, and runs it through the algorithm. Each iteration adds or subtracts a 'feature' from the decision function. So in the end we build up a general detector for faces. The algorithm only keeps the features which work best together not individually, so in the end you have the best 'team' of features. Essentially we are training a function so it can decide what is and is not a face. This is likely what the .dat file output was, a descriptor of the resulting decision function. The more data used to train, the better the decision function will be. 2. Run the decision function against unknown data. Ta da, an answer. It could be right, it could be wrong. Sometimes these algorithms find faces in the darnedest places like walls and other textured surfaces. For more info on Viola-Jones adaptive boosting check out the original paper: Robust Real-time Object Detection http://research.microsoft.com/en-us/um/people/viola/pubs/detect/violajones_ijcv.pdf http://research.microsoft.com/en-us/um/people/viola/pubs/det...