原文：
Labeled Faces in the Wild
Labeled Faces in the Wild is a public benchmark for face verification, also known as pair matching. No matter what the performance of an algorithm on LFW, it should not be used to conclude that an algorithm is suitable for any commercial purpose. There are many reasons for this. Here is a non-exhaustive list:
- Face verification and other forms of face recognition are very different problems. For example, it is very difficult to extrapolate from performance on verification to performance on 1:N recognition.
- Many groups are not well represented in LFW. For example, there are very few children, no babies, very few people over the age of 80, and a relatively small proportion of women. In addition, many ethnicities have very minor representation or none at all.
- While theoretically LFW could be used to assess performance for certain subgroups, the database was not designed to have enough data for strong statistical conclusions about subgroups. Simply put, LFW is not large enough to provide evidence that a particular piece of software has been thoroughly tested.
- Additional conditions, such as poor lighting, extreme pose, strong occlusions, low resolution, and other important factors do not constitute a major part of LFW. These are important areas of evaluation, especially for algorithms designed to recognize images “in the wild”.
For all of these reasons, we would like to emphasize that LFW was published to help the research community make advances in face verification, not to provide a thorough vetting of commercial algorithms before deployment.
While there are many resources available for assessing face recognition algorithms, such as the Face Recognition Vendor Tests run by the USA National Institute of Standards and Technology (NIST), the understanding of how to best test face recognition algorithms for commercial use is a rapidly evolving area. Some of us are actively involved in developing these new standards, and will continue to make them publicly available when they are ready.
译：
Labeled Faces in the Wild
Labeled Faces in the Wild是人脸验证的公共基准，也称为配对匹配。无论一个算法在LFW上的性能如何，都不应该被用来断定一个算法适合任何商业用途。原因有很多。以下是一份非详尽清单：
●人脸验证和其他形式的人脸识别是非常不同的问题。例如，很难从验证时的性能推断到1:N识别的性能。
●许多群体在LFW中没有很好的代表性。比如，孩子很少，没有婴儿，80岁以上的人很少，女性比例相对较小。此外，许多民族的代表性很小或根本没有。
●虽然理论上LFW可用于评估某些子组的性能，但数据库的设计并未提供足够的数据，无法得出关于子组的有力统计结论。简单地说，LFW的规模不足以提供一个特定软件已经过彻底测试的证据。
●其他条件，如光线不足、姿势极端、遮挡强烈、分辨率低和其他重要因素，不构成LFW的主要部分。这些都是重要的评估领域，尤其是那些设计用来识别“野外”图像的算法。
基于所有这些原因，我们想强调的是，LFW的发布是为了帮助研究界在人脸验证方面取得进展，而不是在部署之前对商业算法进行彻底的审查。
虽然有许多资源可用于评估人脸识别算法，例如由美国国家标准与技术研究所（NIST）进行的人脸识别供应商测试，但了解如何最好地测试商业用途的人脸识别算法是一个快速发展的领域。我们中的一些人正在积极参与制定这些新标准，并将在它们准备就绪时继续向公众公布。
链接：获取数据集
