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 this https URL.
Abstract (translated)
行走识别是一种有前景的生物识别方法，旨在通过独特的行走模式识别行人。轮廓表示形式以其易得性、简单的结构、稀疏表示和方便建模而闻名，在实验室控制研究中被广泛应用。然而，随着行走识别从实验室环境迅速转移到现实环境，轮廓表示形式面临着一系列具有挑战性的条件，包括1)无法识别的低质量轮廓（异常分割、严重遮挡或甚至非人类形状），2)可以识别但具有挑战性的轮廓（背景噪声、不标准姿势、轻微遮挡）。为了应对这些挑战，我们重新审视了行走识别流程，并从质量角度出发进行行走识别，即QAGait。具体来说，我们提出了一系列具有成本效益的质评估策略，包括Maxmial Connect Area和模板匹配以消除背景噪声和无法识别的轮廓，以及Alignment策略来处理不标准的姿势。我们还提出了两个质量感知的损失函数，将轮廓质量整合到嵌入空间中的优化。大量实验证明，我们的QAGait可以确保行走的可靠性和性能提升。此外，我们的质评估策略可以无缝地整合到现有的行走数据集中，展示出我们优越的质量。代码可在此处访问：https://www. this URL。
URL
https://arxiv.org/abs/2401.13531
