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The University of Washington’s MegaFace Challenge tested a question that smaller benchmarks could obscure: could an algorithm identify a person’s face when it had to distinguish the match from a million other images? In results reported by IEEE Spectrum in 2016, FaceNet scored 75 percent on one million-face test, while other leading algorithms scored below 60 percent, with some as low as 35 percent. Those are historical benchmark results, not ratings of today’s systems.
What was the MegaFace Challenge?
MegaFace was a University of Washington benchmark designed to evaluate face-recognition algorithms at large scale. Its identification task asked a system to find a matching image among a million distractor photos. Its verification task asked whether two face images showed the same person. These are different questions: identification searches a collection, while verification judges a proposed pair.
The project page lists 1 million distractor photos and 690,572 unique users. It also lists a training set of 4.7 million photos containing 672,057 identities. Those figures describe the project’s datasets, not the number of people in the identification result alone. University of Washington MegaFace project
What did the results show?
In its 2016 report on the initial results, IEEE Spectrum said FaceNet achieved 75 percent on a one-million-face test. Other top algorithms reportedly scored below 60 percent, with some as low as 35 percent. These figures belong to the article’s account of the challenge’s test and should not be read as current accuracy rates or a present-day ranking. IEEE Spectrum’s 2016 report
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The contrast was striking because the same article described the Labeled Faces in the Wild (LFW) benchmark as roughly 13,000 images of 5,000 people, where many algorithms had exceeded 95 percent. A high score on that smaller benchmark did not guarantee similarly strong performance at MegaFace scale. The results concern different benchmark settings; their percentages are not interchangeable.
Why does a million-image search change the test?
In a small collection, an algorithm has fewer alternatives to confuse with the target. Add many distractors and the system has more opportunities to return a lookalike instead of the correct person. That makes large-scale identification a more demanding test of whether a system can find the right match amid a broad gallery.
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As MegaFace investigator Ira Kemelmacher-Shlizerman put it to IEEE Spectrum, “Megaface’s key idea is that algorithms should be evaluated at large scale.” She argued that LFW’s saturation—many algorithms scoring above 95 percent—could give the impression that face recognition was solved, even though performance could look different in a harder, larger search.
What did MegaFace reveal about age and training data?
The 2016 article also reported that matching became more difficult when faces were photographed at different ages, with children presenting particular difficulty. It linked the problem to the limited number of training photos available for children. This is a finding reported in that historical account, not proof that every current system behaves the same way.
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Training data matter because algorithms learn patterns from examples. If some ages or groups are less represented, results on them may differ from results on populations more fully represented in the data. MegaFace’s published training-set count—4.7 million photos across 672,057 identities—describes its training resource, but the headline counts alone do not establish how evenly those examples represented different ages or groups.
How should you compare facial-recognition benchmark scores?
A percentage is meaningful only alongside the conditions under which it was measured. Before comparing results, check:
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- Task: Was the system identifying one person in a gallery or verifying whether two images match?
- Search scale: How many distractors or enrolled identities were included?
- Dataset composition: How many images and identities were used, and what is known about age and population representation?
- Training data: What data could the algorithms use, and were the training conditions comparable?
- Metric and operating point: What exactly does the reported percentage measure, and under what threshold or decision conditions?
The 2016 report gives useful historical results, but it does not supply all details needed to turn its figures into a modern, apples-to-apples comparison. In particular, the available account does not establish the exact operating points or full protocol behind every reported percentage. Avoid using those numbers to claim which system is best now.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can researchers still use the MegaFace dataset?
The University of Washington project page says the challenge has concluded and that its data are no longer distributed. It also says the project’s goals were met. That is the status stated on the official page as of October 5, 2026; availability could change. MegaFace project status
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