Well, I am not sure if this what you are looking for. In general, the performance of any biometric system (e.g fingerprint, voice, facial recognition, etc) is described using several metrics. 

**FAR** or False Acceptance rate is the probability that the system incorrectly authorizes a non-authorized person, due to incorrectly matching the biometric input with a template. The FAR is normally expressed as a percentage, following the FAR definition this is the percentage of invalid inputs which are incorrectly accepted.

**FRR** or False Rejection Rate is the probability that the system incorrectly rejects access to an authorized person, due to failing to match the biometric input with a template. The FRR is normally expressed as a percentage, following the FRR definition this is the percentage of valid inputs which are incorrectly rejected.

**CER** or Crossover Error Rate is the rate where both accept and reject error rates are equal.

**FER** The Failure to Enroll Rate (FER) is the percentage of the population which fails to complete enrollment.

**EXAMPLE:**

let us assume we have a fingerprint biometric system. We also, have **100 users**. During the enrollment stage 5 users where not able to enroll (e.g we can not establish a fingerprint signature/template for them). This mean the system has a **Failure to Enroll Rate (FER) = 5%**. This mean only 95 users can use the system. 


Then, during the testing out of the **95 users**, **10 users** were rejected when the system match their fingerprint against their enrollment fingerprint template. This mean the **FRR =10.52%.**

Also, **3 users** out of the **95 users** were accepted by the system when the system match their fingerprint against other users fingerprint template. This mean the **FAR = 3.15%**

***The lower FAR and FRR , the better the system***