Face-antispoofing expt. using image-quality measures and LDA, on ReplayAttack database
| dev_eer | 0.0633333 |
|---|---|
| dev_eer_threshold | 0.0235956 |
| dev_far | 0.06 |
| dev_frr | 0.0666667 |
| test_far | 0.055 |
| test_frr | 0.15 |
| test_hter | 0.1025 |
| dev_numNegatives | 300 |
| dev_numPositives | 60 |
| test_numNegatives | 400 |
| test_numPositives | 80 |
| dev_scoreDistribution | |
| test_scoreDistribution | |
| dev_roc | |
| test_roc |
The experiment uses 18 image-quality measures (IQM). These IQM are computed for each frame of the input video, and the feature-sets are used to construct a 2-class classifier via Linear Discriminant Analysis (LDA).
The image-quality measures used here form a subset of the measures proposed by Galbally et al:
@INPROCEEDINGS{Galbally_IEEEICPR2014_2014,
author = {Galbally, Javier and Marcel, S{\'{e}}bastien},
title = {Face Anti-spoofing Based on General Image Quality Assessment},
booktitle = {Proceedings of the 22nd International Conference on Pattern Recognition},
month = aug,
year = {2014},
}
| Updated | Name | Actions | |
|---|---|---|---|
| Jan. 5, 2017 | sbhatta/replay_antispoofing (PAD experiments using ReplayAttack database) |