Test results
Accuracy testing
Accuracy of age-estimator, gender-estimator and emotion-estimator
| Service | Accuracy |
| age-estimator | +/- 3.95 years |
| gender-estimator | 97.5% |
| emotion-estimator | 80% |
Accuracy of mask-estimator
Metrics:
- Precision: the metric shows how accurate the service is and represents the number of true positive results relative to all positive results.
- Recall: the metric indicates how completely the service covers the correct results, and represents the number of correct positive results in relation to all the results that should be positive.
- F1 score is one of the ways to combine precision and recall metrics into an aggregate accuracy criterion. F1 score reaches its maximum at recall and precision equal to one, and is close to zero if one of the arguments is close to zero. F1 score is a harmonic mean (with a multiplier of 2, so that in the case of precision = 1 and recall = 1 get F1 = 1).
Results:
| Metric | Value |
| Precision | 0.9967532468 |
| Recall | 0.9903225806 |
| F1 score | 0.9935275081 |
Accuracy of liveness-estimator and deepfake-estimator
Metrics:
- APCER: the metric shows the proportion of validation dataset attacks that were classified as real biometric presentations.
- BPCER: the metric shows the proportion of real biometric presentations classified as attacks.
Results:
| Service | APCER | BPCER |
| liveness-estimator | 2.06% | 1% |
| deepfake-estimator | 5.22% | 1% |