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Except for the USB bug. Apparently to get USB working I'll need to vandalize a USB cable.
Might try that some time, but for now I've connected it to my home router and SSH'ed into it. Examples run fine, now trying simple stuff of my own...
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( 2.9 / 199 )Last week was quite busy with midterms (plus there was nobody in the lab, and I don't have keys :) so I wasn't able to start fiddling with the board. Instead, since there is an obvious winner among the algorithms, I decided to start looking at it.
The basis is described here: http://research.faceplusplus.com/face-r ... -iccv2013/. In short, it uses the cascade of deep convolutional neural networks. Since I'm not doing face recognition and don't need to know the exact shape of an eyebrow, I will only need the first two, at most three.
A few papers that might be useful:
Interesting paper on parallelizing the ConvNet
Not ConvNet, but a lot of implementation details on GPU.
On training ConvNets
Another implementation of a Neural Net on GPU
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( 2.9 / 227 );)
The image set can be downloaded from here.
Full test results as Excel file.
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( 3 / 230 )It has taken more time than I anticipated, partly because I wasn't able to do much during IAC, partly because the existing face detection demos are often hard to find/hard to setup/no longer can be found where they were supposed to be etc. As a result, out of the programs I found, I was only able to test 6. Just to be sure I've mentioned it, I have also removed a few images from the dataset, since they were almost repeats, which I somehow haven't noticed initially.
The short results are below. Just as a quick comment, due to the complexity of the dataset, several algorithms actually got negative score. That was expected, since these algorithms could't detect faces at arbitrary angles.
-FceOnIt ( http://www.idiap.ch/technology-transfer ... s/faceonit ) - 80 hits, 149 misses, 0 false positives, total score = -69
-Real Time Face detector from A. Telnykh ( http://www.torry.net/authorsmore.php?id=6639 ) using tilt face detector - 99 hits, 130 misses, 51 false positive, total score = -81
-OpenCV with Haar features - 96 hits, 133 misses, 50 false positives, ts = -87
-OpenCV with LBP features - 90 hits, 139 misses, 137 false positives, ts = -186
-BetaFace ( http://betaface.com/wpa/index.php/demo-gallery ) - 146 hits, 83 misses, 59 false positives, ts = 4
-Face++ ( http://www.faceplusplus.com/demo-detect/ ) - 139 hits, 90 misses, 3 false positives, ts = 46
So out of these six, I have an obvious winner.
More generally, the algorithms that detect facial features (eyes, nose, mouth etc) - the two last ones - performed much better than the other ones, mainly because they can handle tilted faces. Since for my application it is a must, I suppose that is what I should be focusing on.
I will post more detailed results once I get them into a nicer format...
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The programs that I wanted to try but couldn't are:
-Stasm from U of Capetown - http://www.milbo.users.sonic.net/stasm/ - require specific version of OpenCV that no longer can be downloaded from anywhere
-Face detection from U of Illinois - http://www.ifp.illinois.edu/~antonio/De ... Image.html - whatever image I gave it, just says that it couldn't recognize the format
-Online Face Detector from here: http://www.idiap.ch/~marcel/professional/Welcome.html - (apparently similar to FaceOnIt) - needs admin login (weird...)
-FDLib - http://people.kyb.tuebingen.mpg.de/kien ... /fdlib.htm - they only have precompiled old 32-bit dlls that I couldn't get to run.
P.S. Detailed data now posted.
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( 2.7 / 128 )The final dataset composition:
120 images taken in the lab
90 images of dolls from internet
10 photos from actual rescue missions
80 images of rubble, wood, boxes etc.
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300 in total.
Will post the info about the testing when I return from the International Astronautical Congress in Toronto.
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