Im attempting to perform an analysis on a number of characteristics of a subject to identify them. For any given user, I have anywhere from four to twenty data sets, A, B, C, D... etc, each containing any number of integers (but all of the same length): A = {213, 456, 9802...}, B = {804, 4329, 903...}... etc. These data sets are obtained when the user is registered into the program.

Then, to log in, the same characteristics are measured once each; for example, on one attempt to log in, the computer might get the data set {A=123, B=234, ...etc}.

My question: how can i determine if the user attempting to get in is the same as the one that was registered?

This is assuming that between registration and identification, the characteristics stay about the same; this i have tested and is true.

My current method is to, for each list(A, B, C...), test whether the login value is within one standard deviation of the list values. If this is true for each list, the user is accepted; if any value is outside of the standard deviation, the user is rejected.

What i am looking for is a way to determine:

1) The threshold values for standard deviations, eg 1 standard deviation, 2 standard deviations that the target value should be within

2) The percentage of failed lists that is considered acceptable before the user is rejected

that provides the lowest False Acceptance and False Rejection rates, probably based on the number and/or length of lists.

I know programming, and i know biometrics, but i dont know statistics. It seems like there should be a simple formula for calculating this, and it also seems like my problem can be reduced to something much simpler, but i lack the the statistics experience necessary to figure out what these are. Thank you in advance for your help!