I am new to NNs and I have a question about the convolutional layers in CNN. A convolution layer is said to perform feature extraction or work as a feature extractor in CNN. The first convolutional layer learns to extract low-level features which means that the first layer will convert the original image into multiple copies of the original image (depending on the number of filters used) that contain only the low-level features, neglecting any other features in the original image, right ?
How does the next layers extract other features high level features i.e. faces if they were already neglected by the first layer ?
Why don't we have several separate layers, each of which does a specific extraction of some features and once all the needed has been extracted, the output is merged before the fully connected layer ?
I don't get the point of applying different filters (after the first layer)on the entry already filtered.