This repo contains a Keras implementation of the paper,

VGGFace2: A dataset for recognising faces across pose and age (Cao et al., FG 2018).

• Python 2.7.15
• Keras 2.2.4
• Tensorflow 1.8.0

The dataset used for the experiments are

• VGGFace2 [1]

Keras Model (https://drive.google.com/file/d/1AHVpuB24lKAqNyRRjhX7ABlEor6ByZlS/view?usp=sharing),

This model is trained with a slightly different tight crops, but I have also tested on the tight crops (as we did in the paper), and am able to get similar results (on IJBB).

TAR @ FAR = 1e-5 : 0.64

TAR @ FAR = 1e-4 : 0.78

TAR @ FAR = 1e-3 : 0.88

TAR @ FAR = 1e-2 : 0.94

TAR @ FAR = 1e-1 : 0.98

To test a specific model on the IJB dataset, for example, the model trained with ResNet50 trained by sgd with softmax, and feature dimension 512

• python predict.py --net resnet50 --batch_size 64 --gpu 2 --loss softmax --aggregation avg --resume ../model/resnet50_softmax_dim512/weights.h5 --feature_dim 512

@InProceedings{Cao18,
  author       = "Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman ",
  title        = "VGGFace2: A dataset for recognising face across pose and age",
  booktitle    = "International Conference on Automatic Face and Gesture Recognition, 2018.",
  year         = "2018",
}