4 ms·
Cite please, I'm guessing this is only under controlled conditions, or perhaps more aptly described as "difference recognition."
by rhizome 8y ago
Cite please, I'm guessing this is only under controlled conditions, or perhaps more aptly described as "difference recognition."
- m0zg 8y agoLMGTFY: https://www.google.com/search?q=facial+recognition+exceeds+human+accuracy&oq=facial+recognition+exceeds+human+accuracy&aqs=chrome..69i57.15007j0j7&sourceid=chrome&ie=UTF-8 https://www.google.com/search?q=facial+recognition+exceeds+h... Or, if that's still too much work: http://vis-www.cs.umass.edu/lfw/results.html http://vis-www.cs.umass.edu/lfw/results.html
- deleted 8y ago[deleted]
- rhizome 8y agoI'll have to look into the terminology to be confident of the best match for this context, but I figure "Image-Restricted, Label-Free Outside Data Results"[1] might match the scenario where a recognizer provides a yes/no after being trained on e.g. driver's licenses (let's call it the "Super Bowl" model). None of the results appear to be greater than 95%, which I understand to be a hard ceiling for requiring human QC. Many of the other methods get you worse than 50-50 chance of false positives by the time the algo gets to 95% true positives, but not being a statistician I may be reading the results wrong, in which case I'm totally willing to be corrected. Empirically, though, we know that the Super Bowl model has never worked. 1. http://vis-www.cs.umass.edu/lfw/results.html#ImageRestrictedLF http://vis-www.cs.umass.edu/lfw/results.html#ImageRestricted...