Convolutional neural networks (CNNs) give the state-of-the-art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising ‘errors'. We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face-matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to appear a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face-matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.

卷积神经网络（CNN）在许多模式识别问题中均具有最先进的性能，但可能会受到精心制作的噪声模式的欺骗。我们报告说，CNN人脸识别系统也会产生令人惊讶的“错误”。我们测试了六个商业人脸识别CNN，发现它们在标准人脸匹配任务上胜过典型的人类参与者。但是，他们也声明人类不会匹配，因为这对图像中的一个已被转换为显示不同的性别或种族。这不是由于性能不佳；最佳的CNN在人脸匹配任务上的表现几乎完美，但对于其他明显种族或性别的面孔，也要声明最多的匹配。尽管在性别和种族的重要性上有所不同，但人类和计算机系统的工作方式并没有完全不同。