Are FAR and FRR the same as FPR and FNR, respectively? FPR = False Positive Rate
FNR = False Negative Rate
FAR = False Acceptance Rate
FRR = False Rejection Rate
Are they the same? if Not, is it possible to calculate FAR and FRR from the confusion matrix?
Thank you
 A: Yes, they are the same.
https://books.google.ca/books?id=Go4kBAAAQBAJ&pg=PA195&lpg=PA195&dq=FRR+vs+FNR&source=bl&ots=wZQadPKSIM&sig=fXrSks9EKc_ebkMaDuuXBMMqugM&hl=en&sa=X&ved=0ahUKEwjd9dDkvJrTAhXC5YMKHS1LAIIQ6AEITzAJ#v=onepage&q=FRR%20vs%20FNR&f=false
So in order to calculate their values from the confusion matrix:
FAR = FPR = FP/(FP + TN)

FRR = FNR = FN/(FN + TP)

where FP: False positive
      FN: False Negative
      TN: True Negative
      TP: True Positive

A: If you want to compute FPR and FNR (aka FAR and FRR), here is a Python code for this :
from sklearn import metrics

fpr, tpr, thresholds = metrics.roc_curve(y_true, scores)
fnr = 1-tpr

A: If it helps  you can look at the confusion matrix as below to see that they are the same :
Action -->  Accept    Reject
          |-----------------
Genuine   |  TP    |    FN
          |-----------------
Impostor  |  FP    |    TN

A: Yes, they are the same.
but confusion metrics can switch between positive features and negative features as we need, Biometrics can not do that.
A: No, they are not the same.
FAR and FRR are calculated over all attempts, not individually over positive and negative cases. Ref1 Ref2 Ref3 Ref4

FAR = FP / (TP + FP + TN + FN)
FRR = FN / (TP + FP + TN + FN)

