Genuine and Impostor Distribution with Face Part Blurring

All the code repos required to reproduced this results are put in a google drive link in the section below:

Google Drive Link For All Repos

Note: Make Sure that all the repos - insightface, face_parsing_BiSeNet and face_matching are at the same directory level.

BiSeNet Segmentation

In this project, modified version of BiSeNet Segmentation was used. The orginal implementation of this modified version can be found here. The most relevant python files are put in folder "face_parsing_BiSeNet" and are:

• create_mask.py
• create_blur_part.py
• multiple_parts.py

Example Usage

To generate images with blurred parts, following script with proper inputs are shown below. This scripts takes in directory of images, creates a mask for particular part, saves the mask in intermediate directory, and finally, uses this mask to create the image with blurred part.

python3 ./face_parsing_BiSeNet/multiple_parts.py -s /path_folder_with_images/ -m /intermediate_folder_to_save_mask/ -d /destination_to_save_images

Note:

The original modified implementation of BiSeNet only processes single images. To process the whole directory, the usage was heavily inspired from this github repo.

Feature Extraction

The feature extractor used in this project is from ArcFace Paper and the usage is adapted from repo, which can be found here. There are several options for different architecture based feature extractor, but for this project - ArcFace was used and all the pre-trained model required to reproduce the results provided ** Here ** .

Example Usage

python3 ./face_matching/insightface_feature_extraction.py -s /path_folder_with_images/ -d /destination_to_save_extractions

The feature returned from above feature extraction returns folders with .npy file. To use the results for further steps, create a list with the absolute path of this directory. This link might be helpful. First 5 lines of such list could be something like this :

/usr/Desktop/saved_features/img1.npy
/usr/Desktop/saved_features/img2.npy
/usr/Desktop/saved_features/img3.npy
/usr/Desktop/saved_features/img4.npy
/usr/Desktop/saved_features/img5.npy

Note :

• This setting assumes, input size of (112,112). Add --image-size argument while running the script, with appropriate value, if a different image size is to be used.
• Make sure that there is absolutely no additional lines in the list file(.txt).

Feature Matching

Cosine Similarity is the metrics used to find the similarity between two 512-d representation, which is the ouput from feature extraction used above.

Example usage:

This usage is in case when the both the probe and gallery list are blurred, meaning that you are matching within the list

python3 ./face_matching/feature_match_efficient.py -p ./file_with_abspath_features.txt -o /destination_to_save_match_results -d dataset_name -gr CM

• Dataset for this project : MORPH
• Groups: CM, CF, AAM, AAF
• Save Format: For CM (for example) - results will be saved something like CM_authentic.npy, CM_impostor.npy, CM_labels.txt in the destination folder

To cross-match i.e match images with blur parts with original image, the gallery list can be introduced as follows:

python3 ./face_matching/feature_match_efficient.py -p ./file_with_abspath_probe.txt -g ./file_with_abspath_gallery.txt -o /destination_to_save_match_results -d dataset_name -gr CM

Plots

All the functions used for plotting are inside Plots folder of face_matching repo. The authentic and impostor distribution npy file required for this part is the result of matching done above.

Example Usage

python3 ./face_matching/Plots/plot_relative_freq_histogram_v2.py -a1 ../authentic_dist1.npy -i1 ../impostor_dist1.npy -l1 Label1 -a2 ../authentic_dist2.npy -i2 ../impostor_dist2.npy -l2 Label2 -t 'Tittle' -d ../plot_save_folder -n output

Dataset

The dataset used in this project is MORPH