I was reading about convolutional neural networks and I came across such an explanation:

Consider detecting features in human face. The earlier layers of neural networks learn coarse features such as edges in the images and the latent layers learn more complex ( finer) features such as eyes, nose and etc

I am wondering why this is a true statement, namely how can we know that a neural network first starts by learning primitive features and then learns complex features. Could you please explain?