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The face recognition problem is very tough and we are nowhere close to solving it for most real problems. The results on Labeled Faces in the Wild (LFW) [1] sho

by apu 16y ago

The face recognition problem is very tough and we are nowhere close to solving it for most real problems. The results on Labeled Faces in the Wild (LFW) [1] show that even on the easier "verification" problem -- "are these two images of the same person?" -- the best performance is under 90%. To figure out how this translates to recognition -- "who is this person?" -- you can roughly take the verification rate V and number of different people in your database N and get a recognition rate of roughly V^sqrt(N). (This is a very very rough estimate!) So with 1 person in your database, your rate is just V = ~90%. With 100 people, it's already down to 35%. And so on... (I'm the author of one of the best methods on LFW right now -- the attribute-based one [2].) [1] http://vis-www.cs.umass.edu/lfw/ http://vis-www.cs.umass.edu/lfw/ [2] http://www.cs.columbia.edu/CAVE/projects/faceverification/ http://www.cs.columbia.edu/CAVE/projects/faceverification/

liuliu 16y ago
I am eager to replicate your attribute method but haven't got much time to do so. Very excited to know that you are on Hacker News too. My impression is that "verification" problem is already a leap forward comparing to "identification" problem which consists of fewer people and many samples. The problem space is much interesting now than before with the astronomically number of digital photos available on the Internet (considering that for about 3 years, the "verification" result are improved from 69% to around 85%). But my main concern is that our human being may not be good at "large-scale" face recognition problem after all. If that were true, the fundamental of computer-based face recognition problem would be flawed.
- apu 16y ago
  We have results in our face verification paper showing exactly how well people do on the LFW verification task. The answer is: very good, at 99.2%. However, we also show that much of this is coming from the background and context in the images in LFW. The fact that those images were originally gathered from Yahoo News means that many images are taken at the same event -- with the same background and same clothes, etc., and people can use this to make good guesses, even without seeing the person's face! I think the vision community is thus looking for how to exploit context effectively. There have been a number of interesting approaches recently, from looking at "things" vs "stuff", to using additional information such as transcripts and captions, etc. And of course, there are still lots of people looking at how to better solve the basic recognition problem itself.

by apu 16y ago

liuliu 16y ago

I am eager to replicate your attribute method but haven't got much time to do so. Very excited to know that you are on Hacker News too. My impression is that "verification" problem is already a leap forward comparing to "identification" problem which consists of fewer people and many samples. The problem space is much interesting now than before with the astronomically number of digital photos available on the Internet (considering that for about 3 years, the "verification" result are improved from 69% to around 85%). But my main concern is that our human being may not be good at "large-scale" face recognition problem after all. If that were true, the fundamental of computer-based face recognition problem would be flawed.

apu 16y ago
We have results in our face verification paper showing exactly how well people do on the LFW verification task. The answer is: very good, at 99.2%. However, we also show that much of this is coming from the background and context in the images in LFW. The fact that those images were originally gathered from Yahoo News means that many images are taken at the same event -- with the same background and same clothes, etc., and people can use this to make good guesses, even without seeing the person's face! I think the vision community is thus looking for how to exploit context effectively. There have been a number of interesting approaches recently, from looking at "things" vs "stuff", to using additional information such as transcripts and captions, etc. And of course, there are still lots of people looking at how to better solve the basic recognition problem itself.