A Comprehensive Review of Gait Recognition System for Human Authentication
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This paper presents a comprehensive review of gait recognition systems as a biometric authentication modality. Gait, characterized by unique walking patterns, offers a non-invasive approach to human identification, making it a viable alternative to fingerprint and facial recognition. The study explores recent advancements in machine learning, particularly Graph Convolutional Networks (GCNs) and pose estimation, which enhance the accuracy and robustness of gait recognition. Additionally, the paper introduces the Unified Gait Dataset (UGD), created through dataset fusion and data augmentation techniques, to improve model generalization in real-world scenarios. Despite its potential, gait recognition faces challenges such as sensitivity to environmental factors, clothing variations, and high computational costs. This review discusses emerging research directions aimed at overcoming these limitations and enhancing the applicability of gait recognition in security and healthcare applications.
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