Image classification with extra 'dimension'
I would add it to the CNN output, i.e. the last tensor before the dense layer.
In classification, a CNN is used as a feature extractor. The goal is, given an image, to find the best feature vector that should be passed to the dense layer. So each element represents something for your neural network, a feature.
If you have any other feature you want to add, put it there and let the dense layers do the work.
I am doing image classification based on a CNN in tensorflow and Keras. I am classifying images of faces, into which emotion is being expressed (happy, sad, etc). This actually works pretty well, but there is something I don't really understand. 
For each image, I also have the age of the person that is on the image. I want to add this information (age) to the CNN. So right now the CNN only uses the pixels of the image, but i want to make the CNN to take the age into account. Is this possible / what is the method for this? 
My first thought was to add an extra dimension to the image, with the age, but then I'm kind of stuck... Because age is just 1 number, and of course each layer in the image is width x height. Can I just ad a layer that consists of a whole matrix with just the age as value of each pixel? 
Anyone who knows how such a thing usually is done...?
Thanks in advance