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QAGait: Revisit Gait Recognition from a Quality Perspective (2401.13531v1)

Published 24 Jan 2024 in cs.CV

Abstract: Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

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References (47)
  1. Zero-Shot 3D Shape Correspondence. arXiv preprint arXiv:2306.03253.
  2. Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific and Engineering Research, 3(8): 1–5.
  3. Lagrange motion analysis and view embeddings for improved gait recognition. In CVPR, 20249–20258.
  4. Silhouette-based view-embeddings for gait recognition under multiple views. In ICIP, 2319–2323. IEEE.
  5. GaitSet: Regarding gait as a set for cross-view gait recognition. In AAAI, volume 33, 8126–8133.
  6. GaitSet: Cross-view gait recognition through utilizing gait as a deep set. TPAMI, 44(7): 3467–3478.
  7. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 4690–4699.
  8. Metagait: Learning to learn an omni sample adaptive representation for gait recognition. In ECCV, 357–374. Springer.
  9. GaitGCI: Generative counterfactual intervention for gait recognition. In CVPR, 5578–5588.
  10. GaitMPL: Gait recognition with memory-augmented progressive learning. TIP.
  11. OpenGait: Revisiting gait recognition towards better practicality. In CVPR, 9707–9716.
  12. Gaitpart: Temporal part-based model for gait recognition. In CVPR, 14225–14233.
  13. LiCamGait: Gait recognition in the wild by using LiDAR and camera multi-modal visual sensors. arXiv preprint arXiv:2211.12371.
  14. Gait lateral network: Learning discriminative and compact representations for gait recognition. In ECCV, 382–398. Springer.
  15. Set residual network for silhouette-based gait recognition. TBIOM, 3(3): 384–393.
  16. Gait quality aware network: toward the interpretability of silhouette-based gait recognition. TNNLS.
  17. GaitTAKE: Gait recognition by temporal attention and keypoint-guided embedding. In ICIP, 2546–2550. IEEE.
  18. Context-sensitive temporal feature learning for gait recognition. In ICCV, 12909–12918.
  19. 3D local convolutional neural networks for gait recognition. In ICCV, 14920–14929.
  20. Adaface: Quality adaptive margin for face recognition. In CVPR, 18750–18759.
  21. Lehmann, R. 2013. 3 σ𝜎\sigmaitalic_σ-rule for outlier detection from the viewpoint of geodetic adjustment. Journal of Surveying Engineering, 139(4): 157–165.
  22. Face recognition in low quality images: A survey. arXiv preprint arXiv:1805.11519.
  23. Gait recognition via effective global-local feature representation and local temporal aggregation. In ICCV, 14648–14656.
  24. SMPL: A skinned multi-person linear model. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2, 851–866.
  25. Bag of tricks and a strong baseline for deep person re-identification. In CVPRW.
  26. Dynamic aggregated network for gait recognition. In CVPR, 22076–22085.
  27. Image quality assessment using contrastive learning. TIP, 31: 4149–4161.
  28. Gait. Handbook of Clinical Neurology, 159: 119–134.
  29. Computer vision based quality control for additive manufacturing parts. The International Journal of Advanced Manufacturing Technology, 124(10): 3241–3256.
  30. DACNN: Blind image quality assessment via a distortion-aware convolutional neural network. TCSVT, 32(11): 7518–7531.
  31. GaitMixer: skeleton-based gait representation learning via wide-spectrum multi-axial mixer. In ICASSP, 1–5. IEEE.
  32. Face image quality assessment: A literature survey. ACM Computing Surveys (CSUR), 54(10s): 1–49.
  33. Deep gait recognition: A survey. TPAMI, 45(1): 264–284.
  34. LidarGait: Benchmarking 3D gait recognition with point clouds. In CVPR, 1054–1063.
  35. A comprehensive survey on deep gait recognition: algorithms, datasets and challenges. arXiv preprint arXiv:2206.13732.
  36. Geinet: View-invariant gait recognition using a convolutional neural network. In ICB, 1–8. IEEE.
  37. Towards a deeper understanding of skeleton-based gait recognition. In CVPR, 1569–1577.
  38. GaitGraph: Graph convolutional network for skeleton-based gait recognition. In ICIP, 2314–2318. IEEE.
  39. A survey on gait recognition. ACM Computing Surveys, 51(5): 1–35.
  40. CosFace: Large margin cosine loss for deep face recognition. In CVPR, 5265–5274.
  41. DyGait: Exploiting dynamic representations for high-performance gait recognition. arXiv preprint arXiv:2303.14953.
  42. LandmarkGait: Intrinsic Human Parsing for Gait Recognition. In ACM MM, 2305–2314.
  43. GaitFormer: Revisiting intrinsic periodicity for gait recognition. arXiv preprint arXiv:2307.13259.
  44. Maniqa: Multi-dimension attention network for no-reference image quality assessment. In CVPR, 1191–1200.
  45. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In ICPR, volume 4, 441–444. IEEE.
  46. Gait recognition in the wild with dense 3d representations and a benchmark. In CVPR, 20228–20237.
  47. Gait recognition in the wild: A benchmark. In ICCV, 14789–14799.
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