Basic structure

The structure of the FaceNet facenet recognition system is represented schematically in Figure 1.

Figure 1: Overall structure of the FaceNet face recognition system

For training, the researchers used as input batches of about 1800 images in which for each identity there were about 40 similar images and several randomly sampled images relating to other identities. These batches were fed to a deep convolutional neural network and the network was trained using stochastic gradient descent method with standard backpropagation and the Adaptive Gradient Optimizer (AdaGrad) algorithm. The learning rate was initially set at 0.05 which was later lowered while finalizing the model.

Structure of the CNN

The researchers used used two types of architectures, which they called NN1 and NN2, and explored their trade-offs. The practical differences between the models lie in the difference of parameters and FLOPS. The details of the NN1 model are presented in the table below.

Triplet loss function

The Triplet Loss minimizes the distance between an anchor and a positive, both of which have the same identity, and maximizes the distance between the anchor and a negative of a different identity.

The loss function that was used in the FaceNet system was called the "Triplet Loss Function". This was a novel idea introduced by the developers of the FaceNet system. This function is defined using certain triplets of the form ${\displaystyle (A,P,N)}$ of training images. In this triplet, ${\displaystyle A}$ (called an "anchor image") denotes the image of a person, ${\displaystyle P}$ (called a "positive image") denotes some other image of the person whose image is ${\displaystyle A}$ and ${\displaystyle N}$ (called a "negative image") denotes the image of some other person different from the person whose image is ${\displaystyle A}$. Let ${\displaystyle x}$ be some image and let ${\displaystyle f(x)}$ be the embedding of ${\displaystyle x}$ in the 128-dimensional Euclidean space. It shall be assumed that the L2-norm of ${\displaystyle f(x)}$ is unity. (The L2 norm of a vector ${\displaystyle X}$ in a finite dimensional Euclidean space is denoted by ${\displaystyle \Vert X\Vert }$.) We pick such triplets from the training data set and let there be ${\displaystyle m}$ such triplets and ${\displaystyle (A^{(i)},P^{(i)},N^{(i)})}$ be a typical triplet. The training is to ensure that, after learning, the following condition called the "triplet constraint" should be satisfied by all triplets ${\displaystyle (A^{(i)},P^{(i)},N^{(i)})}$ in the training data set:

${\displaystyle \Vert f(A^{(i)})-f(P^{(i)})\Vert _{2}^{2}+\alpha <\Vert f(A^{(i)})-f(N^{(i)})\Vert _{2}^{2}}$

where ${\displaystyle \alpha }$ is a constant called the margin and its value has to be set manually. Its value has been set as 0.2.

Thus, the function to be minimized is the following function called the triplet loss function:

${\displaystyle L=\sum _{i=1}^{m}\max {\Big (}\Vert f(A^{(i)})-f(P^{(i)})\Vert _{2}^{2}-\Vert f(A^{(i)})-f(N^{(i)})\Vert _{2}^{2}+\alpha ,0{\Big )}}$

Selection of triplets

In general the number of triplets of the form ${\displaystyle (A^{(i)},P^{(i)},N^{(i)})}$ is very large. To make computations faster, the Google researchers considered only those triplets which violate the triplet constraint. For this, for a given anchor image ${\displaystyle A^{(i)}}$ they chose that positive image ${\displaystyle P^{(i)}}$ for which ${\displaystyle \Vert f(A^{(i)})-f(P^{(i)})\Vert _{2}^{2}}$ is maximum (such a positive image was called a "hard positive image") and that negative image ${\displaystyle N^{(i)}}$ for which ${\displaystyle \Vert f(A^{(i)})-f(N^{(i)})\Vert _{2}^{2}}$ is minimum (such a positive image was called a "hard negative image"). since using the whole training data set to determine the hard positive and hard negative images was computationally expensive and infeasible, the researchers experimented with several methods for selecting the triplets.

• Generate triplets offline computing the minimum and maximum on a subset of the data.
• Generate triplets online by selecting the hard positive/negative examples from within a mini-batch.