# 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 is equal to 0.3 s.

### Accuracy of liveness-estimator module

Image Type | Metric | Value |

real face | BPCER | 0.0427664550618109 |

photo | APCER | 0.117647058823529 |

photo without background | APCER | 0.160442600276625 |

replay attack | APCER | 0.246206896551724 |

2D mask | APCER | 0.0386542591267001 |

3D mask | APCER | 0.0250391236306729 |