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A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks

Wafaa Adnan Alsaggaf1, Irfan Mehmood2, Enas Fawai Khairullah1

  • 1Department of Information Technology, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah 23713, Saudi Arabia.

Computational Intelligence and Neuroscience
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Summary

This study introduces a deep learning method using cycle-consistent generative adversarial networks (GANs) to improve gait recognition accuracy in uncooperative environments. The system effectively translates varied walking conditions to normal gait patterns for reliable person identification.

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Gait recognition is a behavioral biometric system for identifying individuals by their walking patterns.
  • Existing gait recognition systems struggle with accuracy in uncooperative environments due to varying walking conditions.
  • Smart surveillance demands high accuracy, even with uncooperative subjects.

Purpose of the Study:

  • To develop a deep learning method for robust gait recognition in uncooperative environments.
  • To address the limitation of gait recognition systems facing diverse and unknown walking conditions.
  • To enhance the accuracy of person identification using gait analysis under dynamic conditions.

Main Methods:

  • A deep learning approach combining Convolutional Neural Networks (CNNs) and cycle-consistent Generative Adversarial Networks (GANs).
  • The method translates gait energy images (GEIs) from various covariate factors to a normal GEI using unsupervised learning.
  • Cycle-consistent GANs with a Cycle Loss function identify individual pairs and generate normalized GEIs for CNN-based person identification.

Main Results:

  • The proposed system achieved excellent results on the publicly available CASIA-B dataset.
  • Demonstrated successful gait recognition even with disturbed and varied walking conditions.
  • The translation of disturbed GEIs to normal GEIs significantly improved recognition accuracy.

Conclusions:

  • The proposed deep learning method effectively handles uncooperative environments in gait recognition.
  • This system offers a viable solution for security applications in sensitive areas.
  • The approach enhances the reliability of behavioral biometric systems for person identification.