Trueface Documentation
Trueface Documentation
ROC Curves

ROC Curves

Model Performance Metrics

True Positive Rate at specified False Positive Rate: IJB-C Dataset

False Positive Rate
LITE_V3
TFV5
TFV5_2
TFV6
TFV7
1 in 1,000
0.97924
0.97827
0.98021
0.97893
0.98359
1 in 10,000
0.96942
0.96733
0.97019
0.96993
0.977704
1 in 100,000
0.95286
0.94657
0.95332
0.95245
0.96661

Receiver Operating Characteristic (ROC) Tables

Use the following ROC curves and tables to choose the optimal operating threshold for your use case. For guidance on how to interpret an ROC curve, consult this blog post.

Below we provide operating similarity-score thresholds based on evaluations run on various datasets. These metrics must be considered representative or illustrative, not controlling, as a Licensee's input data will vary from the tested datasets described herein. All Licensees are encouraged to evaluate performance metrics and thresholds using their environment data.

IJB-C

You can learn about the characteristics of the IJB-C dataset here: https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf

Similarity Score Threshold at False Positive Rates

False Positive Rate
LITE_V3
TFV5
TFV5_2
TFV6
TFV7
1 in 1,000
0.28803
0.26996
0.26620
0.26210
0.22404
1 in 10,000
0.36326
0.34768
0.34423
0.33785
0.29976
1 in 100,000
0.44098
0.44167
0.43454
0.41902
0.38314

Private Dataset [deprecated]

The following ROC curves were generating using a private dataset. The dataset primarily consists of male faces

LITE V2

TAR at FPR 10^-5: 0.9032

LITE

TAR at FPR 10^-5: 0.8487