Researchers benchmarking their facial-recognition systems against Labeled Faces in the Wild are testing for what they call “verification.” Essentially, they’re measuring how good the algorithms are at determining whether two images are of the same person.

研究人员声称，面对“人面数据库”时，他们主要测试该系统的“确认能力”。就本质而言，他们衡量的是这套算法在判断两张照片是否同属一人时到底有多准确。

In December, a team of Chinese researchers also claimed better than 99 percent accuracy on the dataset. Last year, Facebook researchers published a paper boasting better than 97 percent accuracy. The Facebook FB 1.66% paper points to researchers claiming that humans analyzing images in the Labeled Faces dataset only achieve 97.5 percent accuracy.

去年12月，一个中国研究团队也声称，对这套数据库的识别准确率超过99%。去年，Facebook公司的研究人员发表论文称，他们也能做到超过97%的准确率。根据这篇论文援引的一些研究人员的说法，人类对该数据库的识别准确率仅有97.5%。

However, the approach Google’s researchers took goes beyond simply verifying whether two faces are the same. Its system can also put a name to a face—classic facial recognition—and even present collections of faces that look the most similar or the most distinct.

不过，谷歌研究人员采用的方法绝不只是确认两张脸是否一样这么简单。这套系统还能将人名和脸匹配——经典的人脸识别技术，甚至能把看起来最像或最不像的脸归集在一起。

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