Hi all!

Been lurking for a minute and was wondering if someone could help me with the overview of steps I need to take (using Python) to analyze an image dataset and get the eigenface/fischerface/laplacian variables. My problem is that I just can't wrap my head around the "step 1, step 2, step 3" because I feel like I'm either doubling up on something or I'm missing a crucial step. - I've got a method built for calculating pca. Do I run that on each image or over the whole set as one array? -i know I'm supposed to get the grayscale image first before I process anything -im PRETTY sure I need to do eigenvalues before anything but I can't tell if eigenvalues and eigenface are the same thing -im using opencv's sift detector, but I genuinely don't even know if I need to use that for this specific issue

I don't necessarily want the code, I have been drowning myself in tutorial videos to try and understand this better and again, the pieces all seem to be there but for the life of me I can't get this into my thick skull. If anybody can dumb it down into "first then second then third" I would so greatly appreciate it.

Thanks!