Face Identification: Siamese Convolutional Neural Nets

two minds are better than one

If that looks like a graph, you’re right. Neural nets are directed acyclic graphs.

I’ve always had a bit of trouble recognizing faces, but humans are generally quite good at the task. We’re not quite sure how the process works in our brain, but some interesting studies on macaques indicate two interesting things: neurons fire in clusters to recognize ‘feature patterns’, and primates seem to learn the skill by socializing early in life, rather than possessing it innately.

Humans, I would imagine, are even better than apes — bigger brains and evolutionary pressure towards community development might select for the skill. In classic human fashion, I set out to design a tool to compensate for my biological shortcomings.

Convolutional Neural Networks

CNNs power interesting computer vision tech such as surveillance cameras and identity verification. They’re fantastic for object recognition, because they solve some longstanding issues (object clutter, deformation, lighting etc) — they can ‘generalize’ better than earlier nets. They do this (mostly) through convolutional filters.