GAN and DINOv2 Framework for Robust Cross-Condition Gait Recognition - Derea, Zaid (Academia Open, 2026)
General Background: Gait recognition is a remote, non-invasive biometric widely used in forensics, surveillance, and security systems. Specific Background: Deep learning has advanced gait analysis, yet CNN-based approaches struggle with temporal coherence, cross-view variation, and degraded silhouettes. Knowledge Gap: Existing studies typically separate gait synthesis and recognition, with limited use of self-supervised transformers and insufficient joint evaluation of generative quality and identification performance. Aims: This study proposes an integrated framework combining multi-GAN gait reconstruction, DINOv2 vision transformer feature extraction, and CNN-based identity classification. Results: StyleGAN2 produced the most realistic silhouettes (PSNR 31.2 dB, SSIM 0.925, FID 18.3), while DINOv2 yielded highly separable 768-dimensional features, leading to 98.3% classification accuracy across varied walking conditions and strong clustering metrics (NMI 0.891, ARI 0.847). Novelty: The work unifies generative gait synthesis, transformer-guided spatiotemporal representation, and comprehensive evaluation within a single pipeline. Implications: The framework supports reliable biometric verification in forensic investigation, surveillance monitoring, rehabilitation assessment, and real-time security deployment under clothing, view, and carrying variations. Highlights: Joint GAN synthesis and transformer features within one gait recognition pipeline. High-quality silhouette reconstruction linked to superior identity separability. Stable recognition (>94%) under clothing, view, speed, and carrying variations. Keywords: Gait Recognition, GAN, DINOv2, Biometric Identification, Computer Vision