Situation Has Improved (Maybe)

As I’ve been implementing this and actually figuring out how some of the pieces work together, it appears that variability of the data might not be as big of an issue as I thought.  I had originally assumed that the recognition algorithm would produce a single blob of data for each person you want to recognize, and that you’d be matching against that.  Now that I look at it, that doesn’t seem to be the case.  Instead, it looks like it chops each training image down to some constituent particles, then you run through these particles to determine which one is the closest to the image you want to match.  Which means that hopefully variability won’t be a huge problem, since I’m matching to an individual image.

Then again, I’m talking about the 2500-dimension eigenvalue PCA subspace or something like that, so I really have absolutely no idea what I’m talking about.