I am currently researching in the facerecognition field.
And I can not understand how the facenet algorithm handels a new image
They use an euclidean space for image representation. Which means that the elements in this space represent the images (faces). To create this space they use triplet's of images (1 Anchor, 1 Image of the same person (ancher) and a foreign image). These triplets are generated via a data-mining method.
In the end result images of the same person are close together and seperated via a margin. Which means that all images outside this margin belong to a foreign person. So similarity is measured by distance.
If we now want to recognize or verify a new image/face, what exactly is done?
It seems we will need again triplets, so a single image is not even possible? And what happens then? This triplet must be compared somehow again to all existing triplets to find a place for this new image in the euclidean space? If it is whithin a margin that this new image got verified, otherwise its unkown?