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What I strongly suggest for every computer vision project is proper validation. You really need figures to talk about. A lot of computer vision algorithms need a lot of fine tuning to work with a high recognition rate. Especially for face recognition you will need preprocessing to achieve higher recognition rates (for example the proper alignment of faces). What you need for a cross validation is the data you expect in your use case, in the video examples it is the faces found by the cascade classifier.
Once you have collected your data, perform a 10-fold cross validation for example. This will give you a hint of the recognition rate you can expect for the classifier. I have shown how to do this with OpenCV and Python in this post:
If the recognition rate is too low, go for preprocessing. I have written down a list of relevant publications (and source code) in this post:
