So, unless I read the abstract wrong, this is identifying age, race, and gender, then making categorical qualifiers based on those distinctions. While using ML to do the facial recognition to distinguish a person's age, race and gender is neat - categorizing their political affiliation with a 72% accuracy rate is fairly nominal, given the tools used by modern parties to garner donations and directed online advertisin…
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
A face descriptor is obtained from the learned networks as follows: the centre 224 × 224 crop of the face image is used. The shorter side is resized to 256, and the CNNs descriptor is computed for this region by extracting the deep features from the layer adjacent to the classifier layer. This leads to a 2048 dimensional descriptor, which is then L2 normalised.