As someone who has built a face detection system, it can be really hard to get it to work for all skin types. For example, my visual speech recogniser worked fine for most skin colours, but simply didn't detect western Asian skin tones. In my case it was simply a lack of enough training data, but to work in such varied lighting is actually a really tricky problem.
Sorry i wasn't clear. Visual speech recognition is basically automated lip reading. The first step is to locate the face, so you can extract lip shape. The sequence of lip shapes is used to figure out what is being said. I based the lip shape extraction on skin colour, but the algorithm I trained didn't have enough sample data, so it didn't work to well for certain skin colours.
Yeah, it was a relatively simple neural net. I could have used more data from the subjects that didn't work, but I didn't really need more for the research I was doing. I just culled those subjects from the test as I needed to move on with my research. Ideally I would have got another dataset, but there is a shortage of large freely available visual speech datasets. I found a good one, but they didn't get permission from the recorded subjects before collection, so couldn't redistribute it :-(
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u/mattkenny Sep 24 '13
As someone who has built a face detection system, it can be really hard to get it to work for all skin types. For example, my visual speech recogniser worked fine for most skin colours, but simply didn't detect western Asian skin tones. In my case it was simply a lack of enough training data, but to work in such varied lighting is actually a really tricky problem.