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<p>Gait-based human identification has gained significant attention as a reliable biometric approach due to its non-intrusive nature and ability to identify individuals from a distance. Recent advancements in computer vision and deep learning have improved gait recognition performance; however, challenges related to noisy data, inaccurate segmentation, and classification complexity still affect system reliability. To address these issues, this paper presents an improved score-level fused hybrid classifier for gait-based human identification (ISFHC-GHI). Initially, the input gait frames are preprocessed using an adaptive LOG-based Wiener filtering (AdLOG-WF) approach to suppress noise while preserving important image details. The preprocessed frames are then segmented using a Modified U-Net (mU-Net) model designed to retain fine structural information and improve boundary segmentation. Subsequently, multiple discriminative features, including Hierarchy of Skeleton (HOS), Multitexton, and color-based features, are extracted to capture structural, textural, and appearance-related gait characteristics. The extracted features are classified using a hybrid framework based on LinkNet and SqueezeNet classifiers. Finally, an improved score-level fusion strategy combines the classifier outputs to enhance identification performance and classification stability. Experimental analysis conducted on benchmark gait datasets demonstrates that the proposed ISFHC-GHI framework achieves improved performance compared to existing methods, attaining an accuracy of 0.915, precision of 0.870, and NPV of 0.937.</p>

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Hybrid Gait-Based Human Identification Using Modified U-Net and Score-Level Fusion

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Uppuluri Lakshmi Soundharya,
  • Gogineni Krishna Chaitanya,
  • Khadri Syed Faizz Ahmad,
  • R. Srilakshmi,
  • Sasidhar Reddy Gaddam,
  • V. Lakshmi Chetana

摘要

Gait-based human identification has gained significant attention as a reliable biometric approach due to its non-intrusive nature and ability to identify individuals from a distance. Recent advancements in computer vision and deep learning have improved gait recognition performance; however, challenges related to noisy data, inaccurate segmentation, and classification complexity still affect system reliability. To address these issues, this paper presents an improved score-level fused hybrid classifier for gait-based human identification (ISFHC-GHI). Initially, the input gait frames are preprocessed using an adaptive LOG-based Wiener filtering (AdLOG-WF) approach to suppress noise while preserving important image details. The preprocessed frames are then segmented using a Modified U-Net (mU-Net) model designed to retain fine structural information and improve boundary segmentation. Subsequently, multiple discriminative features, including Hierarchy of Skeleton (HOS), Multitexton, and color-based features, are extracted to capture structural, textural, and appearance-related gait characteristics. The extracted features are classified using a hybrid framework based on LinkNet and SqueezeNet classifiers. Finally, an improved score-level fusion strategy combines the classifier outputs to enhance identification performance and classification stability. Experimental analysis conducted on benchmark gait datasets demonstrates that the proposed ISFHC-GHI framework achieves improved performance compared to existing methods, attaining an accuracy of 0.915, precision of 0.870, and NPV of 0.937.