Benchmark Results

Accuracy Testing

Accuracy of age-estimator, gender-estimator and emotion-estimator

Service	Accuracy
age-estimator	+/- 3.95 years
gender-estimator	95%
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 face-detector-liveness-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:

Image Type	Metric	Value
real face	BPCER	0.29981
photo	APCER	0.04911
photo without background	APCER	0.12
replay attack	APCER	0.01339
2D mask	APCER	0.02888
3D mask	APCER	0.01333

note

Average request time equals 0.3 s.