---
title: "Success Measurement"
slug: "biometrics-success-measurement"
updated: 2025-08-22T10:52:06Z
published: 2025-08-22T10:52:06Z
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.knovvu.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Success Measurement

| Document Number | Revision Number | Revision Date |
| --- | --- | --- |
|  |  |  |

## Accuracy of a Biometric System

To evaluate the accuracy and performance of a biometric system, three key metrics are commonly utilized: the False Acceptance Rate (FAR), the False Rejection Rate (FRR), and the Equal Error Rate (EER).

**The False Acceptance Rate (FAR)** measures the rate at which the biometric system incorrectly accepts impostors or unauthorized users. It quantifies the system's vulnerability to impostor attacks and represents the security level of the system. A lower FAR indicates a more secure system.

**The False Rejection Rate (FRR)** measures the rate at which the biometric system incorrectly rejects genuine users. It reflects the level of convenience for the end-user. A higher FRR implies more instances where genuine users are mistakenly denied access, potentially causing inconvenience and frustration.

**The Equal Error Rate (EER)** represents the point where the FAR and FRR intersect. It serves as a threshold that balances the system's ability to distinguish impostors from genuine users. At the EER, the system makes an equal number of false accepts and false rejects. It provides a single measure to compare the performance of the biometric system. A lower EER indicates a more accurate and convenient system.

          **Note:**

          

The optimal values for FAR, FRR, and EER may vary depending on the specific use case and the desired trade-off between security and convenience.

## Measure the Success

**Step 1 - Random Selection:** Randomly select a certain number of authentication/identification operations (N) from the customer environment at different time intervals.

          **Sample Size:**

          

To ensure statistically objective and accurate analysis, it is recommended to examine a sample size of at least 1000 operations (N = 1000).

**Step 2 - Recording Actual Results:** Note the users and their ***actual authentication results*** obtained from the biometric system.

**Step 3 - User Verification:** Listen to the enrollment records of each user and meticulously compare them with their corresponding authentication records. Note the ***expected results*** of the biometric authentication operations for each user.

          **Notes:**

          

- At least 3 different observers examine the records independently without knowledge of the system outputs or each other's assessments.
- The observers label the authentication results as the same person, different person, or indecisive.
- Only cases where the observers unanimously agree on whether the records belong to the same person or different persons are considered.

**Step 4 - Comparison of Expected and Actual Results:** Compare the expected results of the biometric authentication operations with the actual results obtained from the system. Based on this comparison, each operation is labeled as ***Successful***, ***False Accept (FA)***, or ***False Reject (FR)***.

          **Exclusion of Ambiguous Cases and Evaluation of Error Rates**

          

- Cases where speakers cannot be identified with human ear as the same or different person by SESTEK’s examination will be excluded from the evaluation.
- Error rates will be evaluated based on faulty cases where SESTEK and customer agree.

**Step 5 - Calculation of FAR and FRR:** Calculate the FAR and FRR over the N operations. These ratios provide insights into the system's accuracy and reliability.

          **Calculations:**

          

- FAR = (Number of False Accept (FA) operations / N) * 100
- FRR = (Number of False Reject (FR) operations / N) * 100

The table below is provided for recording observations:

| User | Observers | Expected Results | Actual Results | Result |
| --- | --- | --- | --- | --- |
| Test User-1 | Same person | Authentication accepted | Authentication accepted | Successful |
| Test User-2 | Different person | Authentication rejected | Authentication rejected | Successful |
| Test User-3 | Different person | Authentication rejected | Authentication accepted | False Accept |
| Test User-4 | Same person | Authentication accepted | Authentication rejected | False Reject |

- **Test User:** The identifier of the user being tested.
- **Observers:** The observers' assessment of the user's authentication records (same person, different person, indecisive).
- **Expected Results:** The anticipated outcome of the authentication operation.
- **Actual Results:** The observed outcome of the authentication operation.
- **Result:** The final classification of the authentication operation (Successful, False Accept, or False Reject).

          Exclusion of Non-Standard Audio Recordings:

          

In order to maintain the integrity of the assessment, cases involving non-standard audio recordings for enrollment or authentication are not included. Records to be considered non-standard:

- Recordings where the operator's voice is fully or partially heard in the customer channel.
- Conversations that involve speakers other than the main customer.
- Conversations with significant interruptions caused by network issues.
- Speech recordings with severely reduced quality due to environmental factors such as excessive noise.
- Music or tone signals detected as speech by the system, exceeding 10% of the recording duration.