How many thresholds and distance matrix are in Eigenface?
I'll begin with an apology since I will probably not answer your question as you expect (mostly because I don't understand exactly what you are asking).

Let's recap the process of face recognition using Eigenfaces.


take each image of $d_1$ by $d_2$ and treat it as a $d_1\cdot d_2$ vector (images should be in greyscale).
compute the mean of the vectors in hand, i.e. if you have m images, $X_{mean}=\frac{1}{m}\sum_{i=1}^mX_i$ and move your samples such that the a-prior mean is is zero, (this is an important step that is commonly forgotten).
compute the covariance matrix, $X^TX$. I assume in your scenario $d_1\cdot d_2&gt;&gt;m$, so you obtain the same information by finding $XX^T$ eigenvalues, this could improve your running time significantly.
project the $m$ images over the $p$ eigenvectors corresponding to the largest eigenvalues.
Given a new image, project it using the matrix $[u_1,...,u_p]$ and run K-Nearest-Neighbour (it should be more than sufficient for your purpose).


I think your original question is regarding the classification process after you have projected the images over the principal components.
I the algorithm above, I suggest using KNN.

A few notes:


Eigenfaces don't work so well in reality, you must use images with very similar orientation, otherwise you won't achieve good results.
How to choose the k in the nearest neighbour algorithm? it depends on the number of training examples you have, obviously, there is no point of choosing k to be larger then the number of examples per person.
regarding point 3 above, $X^TX\in M_{(d_1\cdot d_2)\ x\ (d_1\cdot d_2)}, XX^T\in M_{mxm}$, so if your images are over 100 by 100, it's practically a must. 
a couple of useful links:

http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html
http://vision.ucsd.edu/content/yale-face-database



Good Luck!

Addition:


http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
After obtaining the first p Eigenfaces (which are the first p eigenvectors of $X^TX$), you have a matrix $O\in M_{(d_1\cdot d_2)\cdot p}$, given a new image $x\in \mathbb{R}^{d_1\cdot d_2}$, project $O^Tx$, this is a vector in $\mathbb{R}^p$.
Now you have your samples in $\mathbb{R}^p$, your original training samples, and a new image that was projected over $O$, you know want to ask yourself how to classify the new image, one way is to decide by finding the closest vector using $L_2$ or $L_1$ this is the same as using KNN with k=1.
I suggest you read about KNN from the link above.


to sum it up:


compute the projection matrix $O$
project your training data $z_i=O^Tx_i$.
given a new sample, $\overline{x}$, compute $\overline{z}=O^T\cdot\overline{x}$
now you have vectors in $\mathbb{R}^p$, you run the KNN which is an iterative way to find the k closest vectors, and classify your new vector as the majority of the k vectors.