Gait Recognition using Deep Residual Networks and Conditional Generative Adversarial Networks-Reference-Cited by-同舟云学术

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Gait Recognition using Deep Residual Networks and Conditional Generative Adversarial Networks

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

Talal Entesar B.¹,Oraibi Zakariya A.²,Wali Ali³

Affiliation:

1. University of Sfax,National School of Electronics,Sfax,Tunisia,3029

2. University of Basrah,College of Education for Pure Sciences,Dept. of Computer Science,Basrah,Iraq

3. University of Sfax,REsearch Groups in Intelligent Machines National Engineering School of Sfax,Sfax,Tunisia,3029

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10196792/10196808/10197028.pdf?arnumber=10197028

Reference25 articles.

1. GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks

2. View invariant gait recognition using only one uniform model

3. Conditional generative adversarial nets;mirza,2014

4. An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection

5. Invariant feature extraction for gait recognition using only one uniform model

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