The accuracy would depend on the classifier you are using once you have the data in the PCA projected space. In the original Turk/Pentland eigenface paper
http://www.face-rec.org/algorithms/PCA/jcn.pdf
they just use kNN / Euclidean distance but a modern implementation might use SVMs e.g. with an rbf kernel as the classifier in the "face space", with C and gamma parameters optimized using a grid search. LibSVM would do that for you and there is a Matlab wrapper available.
Also you should register the faces first i.e. warping the images so they have facial landmarks e.g. eyes, nose, mouyth in a harmonised position across all the dataset? If the images aren't pre-registered then you will get a performance loss. I would expect a performance in the 90s for a dataset of 5 people using Eigenfaces with SVM and pre-registration. That figure is a gut feeling based on prior implementation / performance of past student projects. One thing to note however is your number of training examples is very low - 5 points in a high dimensional space is not much to train a classifier on.