Very Deep Networks Transform Face Recognition — DeepID3 Breaks Records
Researchers took the power of very deep neural networks and tuned them to spot faces, and the results are striking.
They rebuilt two strong designs from top image systems into models that focus on face recognition, so the nets can tell who is who more clearly.
The models are taught in a smart way — told both a person’s identity and if two photos match — so they learn useful details at many stages, not only at the end.
These are the two new architectures, working together as an ensemble, and they reach very high scores: about 99.
53% verification on a popular face test, and around 96% for single-shot identification.
What this means is faces can be matched almost as well as a human in many cases, and the system keeps improving as it learns from more examples.
The approach shows deep learning still has room to grow, and face tech will keep getting sharper, faster, and more reliable with these ideas being used.
Read article comprehensive review in Paperium.net:
DeepID3: Face Recognition with Very Deep Neural Networks
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