Gait Recognition Using CGAN and EfficientNet Deep Neural Networks

Entesar T. Burges, Zakariya A. Oraibi, Ali Wali

2025

Abstract

The objective of gait recognition is to use a visual camera to identify a person from a distance using a visual camera by their distinctive gait. However, the accuracy of this recognition can be impacted by things like carrying a bag and changing clothes. The framework for human gait recognition system presented in this study is based on deep learning and EfficientNet Deep Neural Network. The proposed framework includes three steps. The first step involves extracting silhouettes. The second step involves computing the gait cycle, and the third involves calculating gait energy Depending on the conditional generative adversarial networks and EfficientNet Deep Neural Network. In the first step, silhouette images are extracted using Gaussian mixture-based background algorithm. The segmentation of the gait cycle is estimated by measuring the silhouette’s bounding box’s length and width, then calculating gait energy. Images resulted from the previous stage are used as input to the conditional generative adversarial networks to generate Gait Energy Image (GEI). EfficientNet is employed as an identification discriminator in this work. The suggested framework was evaluated on a challenging gait dataset called CASIA-B, and scored an accuracy of 97.13%. The framework introduced in this paper outperformed techniques in literature in accuracy.

Paper Citation

in Harvard Style

Burges E., Oraibi Z. and Wali A. (2025). Gait Recognition Using CGAN and EfficientNet Deep Neural Networks. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 339-346. DOI: 10.5220/0013138300003912

in Bibtex Style

@conference{visapp25,
author={Entesar Burges and Zakariya Oraibi and Ali Wali},
title={Gait Recognition Using CGAN and EfficientNet Deep Neural Networks},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={339-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013138300003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Gait Recognition Using CGAN and EfficientNet Deep Neural Networks
SN - 978-989-758-728-3
AU - Burges E.
AU - Oraibi Z.
AU - Wali A.
PY - 2025
SP - 339
EP - 346
DO - 10.5220/0013138300003912
PB - SciTePress