I already had the MB-LBP detector but I got the Viola Jones detector working after I realised the weighting factors needed scaling ... (seems to catch me out every time!).
In the mean-time I've also been trying to understand the adaboost machine learning process for some time, and had another look at that. First I tried writing my own detector/learning algorithm using simple LBP codes. Each feature test is a bitmap of LBP codes which are present at a given location. Training is very fast as many iterations can be executed quickly, but I just haven't been able to get a satisfactory result - it kind of works, but just isn't good enough to be useful. I'm also trying to create a detector which would suit GPU execution, as each test is a simple lookup of a pre-calculated LBP table. I think it can probably be done, but it would help if I understood adaboost. Last week I also looked at average of synthetic exact filters as well (it's used in pyvision, although that isn't where I heard of it first), but like most FFT based algorithms although it is neat and academically interesting, in practical use it has some issues.
So I thought i'd try opencv_traincascade instead and generate a MP-LBP detector. I ran many different options, but it was hard to tell if it had gone into a loop or was just taking a long time. But in the end I just couldn't get it to create a deep enough cascade - it kept hitting "Required leaf false alarm rate achieved", so I gave up. Oh opencv_traincascade loads every negative image EVERY time, so once I made it cache all the images in ram instead (a miniscule 100mb or so) I sped it up a good 10x. Still, not much use if it doesn't work. Frustrating at best. I kind of got a result out of it with a depth 10 cascade (mostly of 2 tests each), but it's not much better than my own attempt and it shows there is a bit more magic involved than i'd hoped (I also have a feeling that as MB-LBP features are so descriptive, it's causing problems with the training process). I'm using the data for the eye detector in opencv (the url is in the eye cascade).
So whilst that was running I neatened up the detector code I had, converted a couple more cascades to the simple text format I use in socles, and started on an android application to use it. I wrote a trivial XML loader using JAXB to load in the cascades from OpenCV - I gotta say, when it works JAXB almost makes XML tolerable. I also noticed my custom text-format loader was pretty slow on android too, turns out creating a String from a byte array using the default (UTF8?) charset is slow as shit - forcing US-ASCII sped it up about 3x (or more, a good few seconds to barely noticeable now), although I should really just use a binary format anyway.
Performance at base is on par with the OpenCV code, however when I use a smaller camera preview image the performance increases dramatically as you'd expect (with OpenCV it only made a minor difference for some reason). Together with some muli-threading, I can get face detection running about 10fps on a 640x480 image even with the haar cascade (MB-LBP is about twice as fast as that, but just isn't as solid). And interactively it looks a bit better as the video preview is rendered asynchronously to the processing (that the detection regions lag isn't important in my application). This is using plain old java, so i'm not sure what some jni would accomplish.
Although i'm using summed area tables the implementations are scaling the images rather than using the tables to do the averaging (it's meant to be a big part of using them ...). Fortunately summed area tables are pretty cheap and simple to generate on a CPU but the scaling can be expensive once you get below 1/2 (nearest neighbour isn't good enough). So i'm using a simple mip-map with bi-linear interpolation for the scaling. With plain Java that's a one-off 8ms per frame for the mipmap load, and then a total of 14ms for the scale + SAT generation for all scales. 20% of the time for a haar cascade is a bit steep, but short of using the GPU or JNI is unavoidable.
Actually I was only getting pretty average performance until I removed all the dynamic allocation per-frame. It was hitting GC very hard and that was taking a lot of time - I got about a 3x speedup when I changed the allocation to 'big enough to fit' rather than 'exact every time'. Because i'm parsing a simple cascade, start-up time is much reduced compared to opencv as well. This startup time and GC overhead were the two biggest problems with OpenCV in terms of user experience, and together with the other issues of trying to make a funky API fit within an Android application, and the installation complexity, make it pretty unappealing.
Update: Today I converted the code to C and hooked it up using JNI. It still does a redundant copy or two, but this sped everything up by 2-4x. And that is over the Java which I sped up a bit more than yesterday by pre-calculating the SAT table offsets for each feature as OpenCV does. The detection itself is around 30-40ms per 640x480 frame on the tablet (searching scale factor 1.2, minimum size 64x64, maximum 320x320, search step size 2, using the frontalface_alt haar cascade - the minimum size has the biggest impact on performance as smaller windows mean many more probes).
Bit over it all now ... but I suppose I will eventually try this method with socles since it should be a big improvement for cache coherency - which I think is what is dogging the current implementation. And now I know why I couldn't get it to work last time i tried.
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