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FaceNet directly maps face images to $\mathbb{R}^{128}$ where distances directly correspond to a measure of face similarity. They use a triplet loss function. The triplet is (face of person A, other face of person A, face of person which is not A). Later, this is called (anchor, positive, negative). The loss function is learned and inspired by LMNN. The idea is to minimize the distance between the two images of the same person and maximize the distance to the other persons image. ## LMNN Large Margin Nearest Neighbor (LMNN) is learning a pseudometric $$d(x, y) = (x y) M (x y)^T$$ where $M$ is a positivedefinite matrix. The only difference between a pseudometric and a metric is that $d(x, y) = 0 \Leftrightarrow x = y$ does not hold. ## Curriculum Learning: Triplet selection Show simple examples first, then increase the difficulty. This is done by selecting the triplets. They use the triplets which are *hard*. For the positive example, this means the distance between the anchor and the positive example is high. For the negative example this means the distance between the anchor and the negative example is low. They want to have $$f(x_i^a)  f(x_i^p)_2^2 + \alpha < f(x_i^a)  f(x_i^n)_2^2$$ where $\alpha$ is a margin and $x_i^a$ is the anchor, $x_i^p$ is the positive face example and $x_i^n$ is the negative example. They increase $\alpha$ over time. It is crucial that $f$ maps the images not in the complete $\mathbb{R}^{128}$, but on the unit sphere. Otherwise one could double $\alpha$ by simply making $f' = 2 \cdot f$. ## Tasks * **Face verification**: Is this the same person? * **Face recognition**: Who is this person? ## Datasets * 99.63% accuracy on Labeled FAces in the Wild (LFW) * 95.12% accuracy on YouTube Faces DB ## Network Two models are evaluated: The [Zeiler & Fergus model](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13) and an architecture based on the [Inception model](http://www.shortscience.org/paper?bibtexKey=journals/corr/SzegedyLJSRAEVR14). ## See also * [DeepFace](http://www.shortscience.org/paper?bibtexKey=conf/cvpr/TaigmanYRW14#martinthoma)
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