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I can train a superhuman-performance facial recognition model with consumer hardware sitting in my garage. I don't think you can, but it depends entirely on wh

I can train a superhuman-performance facial recognition model with consumer hardware sitting in my garage. I don't think you can, but it depends entirely on what you mean by "superhuman performance."

m0zg 8y ago
Means it will distinguish faces better than a human in side-by-side testing. This is actually a thing, and it has been for a while. Humans aren't very good at telling if it's "the same guy" or not.
- rhizome 8y ago
  Cite please, I'm guessing this is only under controlled conditions, or perhaps more aptly described as "difference recognition."
  - m0zg 8y ago
    LMGTFY: 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 ago
      I'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...

I can train a superhuman-performance facial recognition model with consumer hardware sitting in my garage. I don't think you can, but it depends entirely on what you mean by "superhuman performance."

m0zg 8y ago

Means it will distinguish faces better than a human in side-by-side testing. This is actually a thing, and it has been for a while. Humans aren't very good at telling if it's "the same guy" or not.

rhizome 8y ago
Cite please, I'm guessing this is only under controlled conditions, or perhaps more aptly described as "difference recognition."
- m0zg 8y ago
  LMGTFY: 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 ago
    I'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...