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Computer algorithm outperforms humans on ‘Labeled Faces in the Wild’ benchmark

Posted April 28, 2014
Computer algorithm outperforms humans on 'Labeled Faces in the Wild' benchmark
Samples of the datasets in the experiments. From left to right: LFW, Multi-PIE, MORPH, Web Images, and Life Photos. Credit: arXiv:1404.3840 [cs.CV]

For the first time a computer has beaten the human average when attempting to discriminate between faces in the Labeled Faces in the Wild (LFW) dataset. The team from China that programmed the computer and trained the software has written a paper describing their efforts and achievements and have uploaded it to the arXiv preprint server.

People are better at recognizing faces than computers—everyone knows that. Not so well known is that computers are slowing catching up—the work by a pair of researchers in China, is proof of that. In their paper Chaochao Lu and Xiaoou Tang note that they have fine-tuned an algorithm used for distinguishing between faces in photographs, but note that the real breakthrough came in the training. In order for a to figure out if the people shown in two different photographs are the same person, the computer has to have a much broader dataset to draw on.

People get better at recognizing faces the more often they see them—scientists aren’t sure why this is exactly, but it clearly has something to do with adding more data as a person is seen from more angles, in different light, using different expressions, while wearing makeup or not, etc.

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