FaceNet

FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.

The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition.

The learning rate was initially set at 0.05, which was later lowered while finalizing the model.

A key innovation of the system was the triplet loss function and its associated mining method.

On the widely used Labeled Faces in the Wild (LFW) dataset, the FaceNet system achieved an accuracy of 99.63% which is the highest score on LFW in the unrestricted with labeled outside data protocol.