Face recognition

Learning a Similarity Metric Discriminatively, with Application to Face Verification (Siamese network)

http://yann.lecun.com/exdb/publis/pdf/chopra-05.pdf

Training a similarity metric from data

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://arxiv.org/abs/1503.03832

f(A(i))f(P(i))22+α<f(A(i))f(N(i))22||f(A^{(i)})-f(P^{(i)})||_2^2 + \alpha \lt ||f(A^{(i)})-f(N^{(i)})||_2^2
Triplet loss=[f(A(i))f(P(i))22f(A(i))f(N(i))22+α]+\text{Triplet loss}= \Big[||f(A^{(i)})-f(P^{(i)})||_2^2 - ||f(A^{(i)})-f(N^{(i)})||_2^2 + \alpha \Big]_+

A(i)A^{(i)} : anchor of i-th triplet, P(i)P^{(i)}: positive example of i-th triplet, N(i)N^{(i)}: negative example of i-th triplet, α\alpha: margin

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