Solution for ChaLearn Face Anti-spoofing Attack Detection Challenge @ CVPR2019 by a.parkin (VisionLabs)
Our method uses a modified network architecture in [1]. As shown on
image, the RGB, Depth and IR inputs are processed by separate streams
followed by the concatenation and fully-connected layers. Differently
from [1] we use aggregation blocks (Agg res2, ...) to aggregate outputs
from multiple layers of the network. We pre-train network weights on
four different tasks for face recognition and gender recognition. We
then fine- tune these networks separately on the training set of the
CASIA-SURF face anti-spoofing dataset. To increase the robustness to
various attacks, we ensemble networks trained on three training folds
and with two initial seeds. Results of our models evaluated separately
and in combination are illustrated in table.
NN1
NN1a
NN2
NN3
NN4
seed
Val trp@fpr=10e-4
Test trp@fpr=10e-4
✔️
0.9943
✔️
0.9987
✔️
0.9870
✔️
0.9963
✔️
0.9933
✔️
✔️
0.9963
✔️
✔️
✔️
0.9983
✔️
✔️
✔️
✔️
0.9997
✔️
✔️
✔️
✔️
✔️
1.0000
✔️
✔️
✔️
✔️
✔️
1.0000
0.9988
References
[1] Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Ser-
gio Escalera, Hailin Shi, Zezheng Wang, Stan Z. Li, ”CASIA-SURF: A
Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing”,
arXiv, 2018.
Environment
Сreating the conda environment and installing the required libraries