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This is Scientific American ― 60-Second Science. I'm Christopher Intagliata.
Google and Facebook both do a nice job identifying your friends in photos―
a testament to how good machines have gotten at studying human faces.
But how well would an algorithm fare, when pitted against a forensic facial examiner... the experts that testify in court?
"Well it turns out the best algorithm is comparable to the best humans."
Jonathan Phillips, a facial recognition scientist at the National Institute of Standards and Technology.
He and his colleagues presented 20 very difficult image pairs to human experts, and a range of algorithms.
And the most up-to-date algorithms did indeed perform as well as the skilled humans.
But when Phillips and his team asked for input from two humans, or a human and an algorithm,
it was the combined judgment of humans and machines that won out... providing near perfect results.
Which suggests the pooled strengths and weaknesses of human brains and computer code add up to superior accuracy.
The study is in the Proceedings of the National Academy of Sciences.
Phillips says now it's now up to the facial recognition community to use these findings to improve the tests in real-world settings.
And don't worry: human recognizers won't be out of a job anytime soon.
"Just because an algorithm says I give you a high score you don't just accept the word of that black box...
you develop ways of integrating human judgment into the decision you get out of an algorithm itself."
这里是科学美国人――60秒科学。我是克里斯托弗・因塔利亚塔。
在用照片识别出你的朋友这方面,谷歌和脸谱网的表现都很不错,
这证明机器在研究人脸方面拥有突出能力。
那在与面部检察官这种专家在法庭对质时,算法的表现又如何呢?
“事实证明,最好的算法可以与最优秀的人类专家相媲美。”
乔纳森・菲利普斯是美国国家标准与技术研究院的面部识别科学家。
他和同事给人类专家和一系列算法提出了20个极为难识别的图像对。
最先进算法的表现的确和技术过硬的人类专家一样出色。
而当菲利普斯和团队让两个人一组或一个人与一个算法一组进行图像识别时,
结果发现人类与机器的联合判断胜出,其识别结果近乎完美。
这表明,人脑与计算机代码的综合优劣势互补之后,得出的结果具有卓越的精确性。
这项研究结果发表在《美国国家科学院学报》上。
菲利普斯说,现在使用面部识别的团体应该利用这些研究发现来改善现实世界中的测试。
不过别担心:人类识别者短时间内不会失业。
“只因一个算法给了你高分,而你不接受那个未知系统的判断,
就开发出一种方法,将人类判断融入算法所得出的结论中。”
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