Abstract
Gait recognition is to seek correct matches for query individuals by their unique walking patterns at a long distance. However, current methods focus solely on individual gait features, disregarding inter-personal relationships. In this paper, we reconsider gait representation, asserting that gait is not just an aggregation of individual features, but also the relationships among different subjects' gait features once reference gaits are established. From this perspective, we redefine classifier weights as reference-anchored gaits, allowing each person's gait to be described by their relationship with these references. In our work, we call this novel descriptor Relationship Descriptor (RD). This Relationship Descriptor offers two benefits: emphasizing meaningful features and enhancing robustness. To be specific, The normalized dot product between gait features and classifier weights signifies a similarity relation, where each dimension indicates the similarity between the test sample and each training ID's gait prototype, respectively. Despite its potential, the direct use of relationship descriptors poses dimensionality challenges since the dimension of RD depends on the training set's identity count. To address this, we propose a Farthest Anchored gaits Selection algorithm and a dimension reduction method to boost gait recognition performance. Our method can be built on top of off-the-shelf pre-trained classification-based models without extra parameters. We show that RD achieves higher recognition performance than directly using extracted features. We evaluate the effectiveness of our method on the popular GREW, Gait3D, CASIA-B, and OU-MVLP, showing that our method consistently outperforms the baselines and achieves state-of-the-art performances.
Abstract (translated)
步态识别的目标是通过其独特的步态模式,在远距离上寻找与查询个体的正确匹配。然而,当前的方法主要关注个体步态特征,忽略了人际关系。在本文中,我们重新考虑步态表示,断言步态不仅是个体特征的聚合,而且不同个体步态特征之间的关系。从这个角度来看,我们重新定义了分类器权重为参考锚定的步态,允许每个人通过与他们参考之间的关系来描述他们的步态。在我们的工作中,我们称之为新描述符关系描述符(RD)。这种关系描述符提供了两个好处:强调有意义的特征并增强鲁棒性。具体来说,步态特征和分类器权重的等方差表示相似性关系,其中每个维度表示测试样本和每个训练ID的步态原型的相似性。尽管它的潜在性,直接使用关系描述符会面临维度挑战,因为RD维度取决于训练集的身份计数。为了解决这个问题,我们提出了一个参考锚定步态选择算法和维度减少方法,以增强步态识别性能。我们的方法可以建立在普通的预训练分类模型之上,而不需要额外的参数。我们表明,RD比直接使用提取的特征实现更高的识别性能。我们评估了流行的GREW、步态3D、中华亚洲B和OU-MVLP等流行的应用程序,表明我们的方法 consistently outperform 基准模型并实现了最先进的性能。
URL
https://arxiv.org/abs/2308.11487
