FaceNet is a deep convolutional network designed by Google, trained to solve face verification, recognition, and clustering problem with efficiently at scale.
1. directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.
2. optimize the embedding face recognition performance using only 128-bytes per face.
3. achieves the accuracy of 99.63% on Labeled Faces in the Wild (LFW) dataset, and 95.12% on YouTube Faces DB.
1. directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.
2. optimize the embedding face recognition performance using only 128-bytes per face.
3. achieves the accuracy of 99.63% on Labeled Faces in the Wild (LFW) dataset, and 95.12% on YouTube Faces DB.