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Comments (21)
Clayton Burnett
Just wondering how this might benefit LPR tech...even if it gave me a better guess to work off of, it could be very beneficial to me/the police.
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Matt Bischof
Very interesting... but nuts to court-admissibility.
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Undisclosed Manufacturer #1
Cool stuff, but sounds like a court admissibility nightmare...
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Matt Bischof
Considering Apple's facial recognition capabilities in iPhone 6 and up, were a guy to integrate into their databases (fat chance), parse their catalogs and compare users' iCloud photos with video/images captured via surveillance, you'd be on to something.
I mean... who's to say we haven't all already agreed to this in Apple's 56-page TOS agreement we're so quick to scroll through and click 'Agree'?
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Undisclosed Integrator #3
Do you think it will be possible to pass the footage from any camera to this algorithm? Because if that is the case you could use post analytics on all your cameras on site and cross compare the "guesses" from each camera to get real close to having the actual plate.
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Robert Sears
This is real scary stuff.
A captured image has to be proven to be unaltered for it to be acceptable as legal evidence yet we should trust google to decide what the final extrapolated image really is based on what computers says it should be... if they can do this then they can also make that image appear to be anything they want it to be and it does not meet the evidence requirement.. changing the evidenciary requirement would be a huge can of worms and dangerous to those google or the gov doesnt like. Think of the movie "Enemy of the State" or similar.. No Thanks Google.. design better cameras and lenses.
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Timothy Howell
All
Interesting discussion but I am skeptical. More research and especially better training needs to be done. Now I do love technology and I especially like AI neural networks and cognitive processing...
The easiest part for the algorithm to process should be those parts of the image with the greatest contrast. For example the dark pupil and iris set against each other and the iris set against the white portions of the eye should be much more accurate. When I look at the middle image, the algorithm did not get the direction of the eyes correct and when I look at the bottom image, the make up the woman is wearing appears to fool the algorithm again. In looking at the contrast between the red lips and white teeth in the top and bottom pictures, this seems to be a little better. The shapes of the lips and mouth are more consistent between the algorithm and actual image. What I expect to see in all three pictures (or at lease the top two) is better definition between the cheeks the dark background beyond. I do not see this, especially in the top image where the algorithms missed the jaw line shape badly. Missing this badly changes the perception of the face substantially.... The top image looks like two different people with the actual image giving the impression of being much younger in age that the algorithm guess. All three people in the pictures appear to be looking in different directions in the algorithm than in the actual images although as mentioned, the lower image is much less so that the others... I would like to know more about how this algorithm works, I can guess but....
Another issue is that the results of the algorithm can be greatly effected by the initial scanning resolution of the picture (as opposed to the initial resolution of a low resolution camera). If color assignment is done inaccurately during the quantization (A to D conversion) process, it seems to me that would throw the algorithm off as well.
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Henry Detmold
To quote from the conclusion:
Hence there is no evidentiary application, but there may well be intelligence or investigation applications, since a trivial modification could output all plausible candidates for a given low-res image. For example if an investigator somehow knows that the people in an area at a given time are members of a small set of known individuals he may be able to use this to guide further investigation (via different modalities) as to which of that small set is likely in a given low-res image.
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