How face recognition algorithms works

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I've already seen many articles and report about them, but they're a dimension reduction often use as a feature extractor (or even as classifier). My question may not be so clear, but what I'm asking for is the specific opencv implementation: the eigenfaces is the PCA reduction, but which classifier are they using? fisherface is an LDA reduction, but the classifier? same thing for HLBP. By the way I'll take a look anyway to your articles...read always help. (the first and the third link are broken)

Eigenfaces uses k-NN with k=1, Fisherfaces can be seen as an extension of Eigenfaces when each subject in training set have more than 1 sample(image), classifier is also k-NN but k depends on how many samples per person you have in the gallery. Eigenfaces and Fisherfaces are holistic approaches. LBP is a facial feature extraction and the classifier is k-NN with Chi Square distance. What I mean is that if you really master Eigenfaces, you will find Fisherfaces, lbp, svm easier to understand.

Thanks! now I understand! So it's always a KNN classifier, good to know. This mean that if I'd like to implement a PCA extractor with other classifier I need to write it down from the beginning I suppose...