Also, the essence of it is sampling the "labels" (the people's face ID) similar to Word2Vec through similarity. When applied to people's face ID, the key phrase is "In feature space, faces of a feather flock together".

Then, use a recurrent ConvNet at the end to pump out probabilities for each class (in this case face) with a low/tractable number of parameters.

Spent a couple of hours of interesting reading. It sounds like faces cluster in high dimensional space. From the perspective of a curious spectator I would love to see a blog post or article that answered:

What would pairs of maximally distant faces look like?

What would the mediods of a large dataset that was clustered into two clusters look like? Would it be a high level feature that we'd recognize like male or female?

Would faces that were mediods of the cluster be considered more attractive?

Would it be possible to build a similar app that allowed you to identify an individual from memory? Cluster the dataset and present the user with images representative of the mediods of the clusters. Discard images from clusters that weren't selected and re-cluster. Repeat until a handful of images remain.

Could that technique work as a virtual sketch artist and identify a person's doppelganger?

Deep, Derp, et al.

:)

Tapabrata Ghosh