You can use open source libraries such as opencv/torch/torchvision and libraries in R for the machine learning. Haar recognizer is one such algorithm in opencv/

You can use open source libraries such as opencv/torch/torchvision and libraries in R for the machine learning. Haar recognizer is one such algorithm in opencv/torchvision that can locate faces in images. As for the complexity, that's what the market would sort out. People would build on other's work, and everyone would get compensated for as much as their work is used. Numenta is an example of the monolithic comapny doing all the work, and that's why you can't use or build on any of their technology yet (incidentally, they use bayesian type algorithms while i'm advocating easy to use standard algorithms that have many open source implementations - boosting, random forests, support vector machines, and maybe neural networks). A startup opportunity would be for a company to host the learning algorithms, and just have people pay for and submit data from which classifiers (academic jargon for recongizers) can be built and returned. (The company would have to be trusted though as they could simply keep a copy of the created classifier and resell it).