The final chapter of this book has really rounded up our experience and made us combine a variety of our skills to arrive at an end-to-end app that consists of both object detection and object recognition. We became familiar with a range of pre-trained cascade classifiers that OpenCV has to offer, collected our very own training dataset, learned about multi-layer perceptrons, and trained them to recognize emotional expressions in faces. Well, at least my face.

The classifier was undoubtedly able to benefit from the fact that I was the only subject in the dataset, but with all the knowledge and experience that you have gathered with this book, it is now time to overcome these limitations! You can start small and train the classifier on images of you indoors and outdoors, at night and day, during summer and winter. Or, maybe, you are anxious to tackle a real-world dataset and be part of Kaggle's Facial Expression Recognition challenge (see https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge).

If you are into machine learning, you might already know that there is a variety of accessible libraries out there, such as pylearn (https://github.com/lisa-lab/pylearn2), scikit-learn (http://scikit-learn.org), and pycaffe (http://caffe.berkeleyvision.org). Deep learning enthusiasts might want to look into Theano (http://deeplearning.net/software/theano) or Torch (http://torch.ch). Finally, if you find yourself stuck with all these algorithms and no datasets to apply them to, make sure to stop by the UC Irvine Machine Learning Repository (http://archive.ics.uci.edu/ml).

Congratulations! You are now an OpenCV expert.