kushal-vangara / face_matching

Face Matching Repository

Face Matching Repository

Extract features with any of the following:

• vggface_feature_extraction
• facenet_feature_extraction
• lbp_feature_extraction

Example usage:

python3 vggface_feature_extraction.py -n resnet50 -s ../image_list.txt -d ../output_folder

The default weights for VGGFace and ResNet50 are trained on VGGFace and VGGFace2, respectively.

Match with:

• mult_feature_match_list

Example usage:

python3 mult_feature_match_list.py -p ../probe_list.txt -o ../output_results/ -d MORPH -gr AA -m 1

Plot ROC, FMR/FNMR, and histograms using the plot functions inside the plot folder.

Example usage:

python3 plot_relative_freq_histogram.py -a1 ../authentic_dist1.txt -i1 ../impostor_dist1.txt -l1 Label1 -a2 ../authentic_dist2.txt -i2 ../impostor_dist2.txt -l2 Label2 -t 'Tittle' -d ../save_folder -n output

Related papers:

O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In BMVC, 2015.

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman. Vggface2: A dataset for recognising faces across pose and age. In Face and Gesture Recognition, 2018.

Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In European Conference on Computer Vision, 2016.

X. Tan and B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 2010.

Deng, Jiankang, et al. Arcface: Additive angular margin loss for deep face recognition. IEEE Conference on Computer Vision and Pattern Recognition. 2019.