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Code example for Human Identification at a Distance 2020 (HID2020)

overview

Overview

This code example is made for HID2020 Competition. The goal of the competition is to provide an evaluation for state-of-the-arts on human identification at a distance (HID). The competition and workshop is endorsed by IAPR Technical Committee on Biometrics (TC4). The workshop will be hosted in conjunction with the Asian Conference on Computer Vision (ACCV 2020) from Nov 30 – Dec 4, 2020.

How to train you own recognition model?

  1. Set you own model struture in models/model.py and models/model_factor.py.

  2. Configure model training config, the default config file is ./config/baseline_config.yml.

  3. Run code:

bash train.sh

How to test and generate a submission.csv?

Configure the model used for the test and the path of SampleSubmission.csv file.

Run code :

bash test.sh

./config/baseline_config.yml can achieve about 20% accuracy.

And the output file is submission.csv. BEFORE you submit it to CodaLab, make sure you compress it into a zip file.

Citation

The code model refers to the following article. Please cite this paper in your publications if it helps your research:

@article{zhang2019comprehensive, title={A comprehensive study on gait biometrics using a joint CNN-based method}, author={Zhang, Yuqi and Huang, Yongzhen and Wang, Liang and Yu, Shiqi}, journal={Pattern Recognition}, volume={93}, pages={228--236}, year={2019}, publisher={Elsevier} }

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The baseline code for the HID 2020 competition

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