In short: we know that eye-blinks are reflected frontally in the EEG data and we use that knowledge to identify which components reflect for example eye-blinks. It would not make sense to identify a component related to eye-blinks on the back of the head - there would be something wrong with your data.
What ICA does is (data driven) estimate a number of statistically independent components (if you have 64 channels you get 64 components) based on your data. The input data is usually in the form of channels X timepoints X trials.
Eventually the output of ICA gives you, for each component, a weight for each channel which you can use to create the component scalp map. Together, the component scalp maps, the time course of the components, and frequency information of the component provide supplementary information that you can use to identify artifacts such as (but not limited to) eye-blinks, horizontal eye movements and heart beat.
In the above figure component 14 and 15 do not seem to be eye artifacts. (1) they are very focal, which is probably related to noise specific to that channel. (2) They look to be completely uncorrelated with the eye blinks in the original signal.