Convolutional neural net face recognition works in non-human-like ways
This reveals a critical flaw in commercial face recognition systems, highlighting biases that could impact security and fairness applications.
The study found that while convolutional neural networks (CNNs) outperform humans on standard face matching tasks, they also incorrectly match faces transformed to appear as a different sex or race, which humans would not, despite both systems showing agreement on difficult image pairs.
Convolutional neural networks (CNNs) give 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 look 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.