Recent face recognition experiments on a major benchmark LFW show stunning
performance--a number of algorithms achieve near to perfect score, surpassing
human recognition rates. In this paper, we advocate evaluations at the million
scale (LFW includes only 13K photos of 5K people). To this end, we have
assembled the MegaFace dataset and created the first MegaFace challenge. Our
dataset includes One Million photos that capture more than 690K different
individuals. The challenge evaluates performance of algorithms with increasing
numbers of distractors (going from 10 to 1M) in the gallery set. We present
both identification and verification performance, evaluate performance with
respect to pose and a person's age, and compare as a function of training data
size (number of photos and people). We report results of state of the art and
baseline algorithms. Our key observations are that testing at the million scale
reveals big performance differences (of algorithms that perform similarly well
on smaller scale) and that age invariant recognition as well as pose are still
challenging for most. The MegaFace dataset, baseline code, and evaluation
scripts, are all publicly released for further experimentations at:
megaface.cs.washington.edu.

论文提出了 MegaFace 数据集和 MegaFace 挑战，通过增加 gallery 集中的干扰数，评估算法的性能，结果显示在万级别规模下测试揭示了算法之间的大量性能差异，并且年龄和姿态不变性对大多数算法仍然具有挑战性。