Summary
By now, you should have a good understanding of how face detection and face recognition work and how to implement them in Python and OpenCV 5.
The accuracy of detection and recognition algorithms heavily depends on the quality of the training data, so make sure you provide your applications with a large number of training images covering a variety of expressions, poses, and lighting conditions. Later in this book, in Chapter 11, Neutral Networks with OpenCV – an Introduction, we will look at how to use several robust, pre-trained face detection models that build atop advanced algorithms and large sets of training data.
As human beings, we might be predisposed to think that human faces are particularly recognizable. We might even be overconfident in our own face recognition abilities. However, in computer vision, there is nothing very special about human faces, and we can just as readily use algorithms to find and identify other things. We will begin to do so next in Chapter...
