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Free Lunch for Gait Recognition: A Novel Relation Descriptor

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15096))

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Abstract

Gait recognition is to seek correct matches for query individuals by their unique walking patterns. However, current methods focus solely on extracting individual-specific features, overlooking “interpersonal” relationships. In this paper, we propose a novel Relation Descriptor that captures not only individual features but also relations between test gaits and pre-selected gait anchors. Specifically, we reinterpret classifier weights as gait anchors and compute similarity scores between test features and these anchors, which re-expresses individual gait features into a similarity relation distribution. In essence, the relation descriptor offers a holistic perspective that leverages the collective knowledge stored within the classifier’s weights, emphasizing meaningful patterns and enhancing robustness. Despite its potential, relation descriptor poses dimensionality challenges since its dimension depends on the training set’s identity count. To address this, we propose Farthest gait-Anchor Selection to identify the most discriminative gait anchors and an Orthogonal Regularization Loss to increase diversity within gait anchors. Compared to individual-specific features extracted from the backbone, our relation descriptor can boost the performance nearly without any extra costs. We evaluate the effectiveness of our method on the popular GREW, Gait3D, OU-MVLP, CASIA-B, and CCPG, showing that our method consistently outperforms the baselines and achieves state-of-the-art performance.

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Acknowledgment

This work was jointly supported by National Key R&D Program of China (2022ZD0117900), National Natural Science Foundation of China (62236010, 62322607, 62276261, 62276025 and 62206022), Beijing Municipal Science & Technology Commission (Z231100007423015) and Shenzhen Technology Plan Program (KQTD20170331093217368).

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Jilong Wang & Tianzhu Zhang

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jilong Wang, Yan Huang & Liang Wang

  3. Beijing Normal University, Beijing, China

    Saihui Hou & Yongzhen Huang

  4. WATRIX.AI, Beijing, China

    Jilong Wang, Saihui Hou, Chunshui Cao, Xu Liu & Yongzhen Huang

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  1. Jilong Wang
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  2. Saihui Hou
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Corresponding author

Correspondence to Liang Wang .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Wang, J. et al. (2025). Free Lunch for Gait Recognition: A Novel Relation Descriptor. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15096. Springer, Cham. https://doi.org/10.1007/978-3-031-72920-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-72920-1_3

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