# FaceNet - [FaceNet: a Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832) - mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity - Optimize the [Embedding](Embedding.md) itself - FaceNet directly trains its output to be a compact 128-D [Embedding](Embedding.md) using a [Triplet Loss](Triplet%20Loss.md) function - Choosing which triplets to use turns out to be very important for achieving good performance - inspired by [Curriculum Learning](Curriculum%20Learning.md) - online negative exemplar mining strategy which ensures consistently increasing difficulty of triplets as the network trains - also explore hard-positive mining techniques which encourage spherical clusters for the embeddings of a single person - squared [Lp Regularization](Lp%20Regularization.md) L2 distance, in the [Embedding](Embedding.md) space directly correspond to face similarity: faces of the same person have small distances and faces of distinct people have large distances - face verification simply involves thresholding the distance between the two embeddings; recognition becomes a [KNN](KNN) classification problem - [Labeled Faces in the Wild](Labeled%20Faces%20in%20the%20Wild.md) - [Zeiler Fergus](Zeiler%20Fergus.md) - [Inception](Inception.md) - [Harmonic Embedding](Harmonic%20Embedding)