- pytorch >= 1.10
- torchvision
- pyyaml
- tensorboard
- opencv-python
- tqdm
- py7zr
- kornia
- Latest version of C++.
$ git clone https://github.com/ShiqiYu/OpenGait.git
Ensure that OpenGait and its dependecies are installed correctly.
Follow through OpenGait preprocessing instructions for the OU-MVLP dataset and place the .pkl file in the ./data folder.
$ mv PATH_TO_PKL_FILE ./data/OUMVLP.pkl
Apply our modifications to OpenGait to enable gender analysis:
$ bash ./src/move_files.sh
This script will create the score matrices required for gender analysis:
bash ./OpenGait/test.sh
To replicate the results discussed in the paper, run the following commands:
jupyter nbconvert --execute src/split.ipynb --to html # generate random strata
bash ./src/experiment.sh # compute genuine/impostor distributions
jupyter nbconvert --execute src/plot.ipynb --to html # plot the results
This will create a file plot.html with the results in the same format as presented in the paper (histograms, ROC curves, heatmaps, and
Please cite our work, as well as OpenGait.
@InProceedings{Ibragimov_2024_FG,
author = {Ibragimov, Azim and Pamplona Segundo, Mauricio and Sarkar, Sudeep and Bowyer, Kevin},
title = {Unveiling Gender Effects in Gait Recognition using Conditional-Matched Bootstrap Analysis},
month = {May},
year = {2024},
}
@InProceedings{Fan_2023_CVPR,
author = {Fan, Chao and Liang, Junhao and Shen, Chuanfu and Hou, Saihui and Huang, Yongzhen and Yu, Shiqi},
title = {OpenGait: Revisiting Gait Recognition Towards Better Practicality},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {9707-9716}
}