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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 "v
by 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 agoWe 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.