# How is the evaluation setup for YouTube faces of FaceNet?

The YouTube Faces database (YTF) consists of 3,425 videos of 1,595 different people. Given two videos, the task for YTF is to decide if they contain the same person or not. Having $n$ comparisons, the classifier might get $c \leq n$ right. Then the accuracy would be $\frac{c}{n}$.

FaceNet is a CNN which maps an image of a face on a unit sphere of $\mathbb{R}^{128}$. It was evaluated on YTF. How did they decide which person is in the video?

(I can imagine several procedures how this could be done, but I couldn't find it in the paper. One example, how it could be done, is by evaluating all images $x_i^{(k)}$ with $i = 1, \dots, \text{length of video }k$ and averaging the results - but I would like to know what they did / how this is usually done.)