The BEAT platform attests that the following results were obtained by an experiment performed on our servers. We kept all the details needed to reproduce them (toolchain, database, algorithms, libraries, dataformats and the actual experimental setup).
Published 7 years, 5 months ago, on June 29, 2017, 2:51 p.m.
Experiment: sbhatta/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms
Description: Face antispoofing using comparison of LBP histograms
| dev_eer | 0.366667 |
|---|---|
| dev_eer_threshold | -0.190719 |
| dev_far | 0.366667 |
| dev_frr | 0.366667 |
| test_far | 0.34 |
| test_frr | 0.45 |
| test_hter | 0.395 |
| dev_numNegatives | 300 |
| dev_numPositives | 60 |
| test_numNegatives | 400 |
| test_numPositives | 80 |
| dev_scoreDistribution | |
| test_scoreDistribution | |
| dev_roc | |
| test_roc |
Histograms of LBP features are computed for the samples in the training-set. From these histograms, mean-histograms are computed for each of the two classes: real, and attack. Given a new probe, a histograms of LBP features is computed for the probe, and this is compared with the mean-histograms of the two classes. The probe is assigned to the class closest to it, based on the chi-square distance for histogram-similarity.
This method is described in the paper by Chingovska et al:
@INPROCEEDINGS{Chingovska_IEEEBIOSIG2012_2012,
author = {Chingovska, Ivana and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
projects = {Idiap, TABULA RASA},
month = sep,
title = {On the Effectiveness of Local Binary Patterns in Face Anti-spoofing},
booktitle = {Proceedings of the 11th International Conference of the Biometrics Special Interest Group},
year = {2012},
}