Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild).

翻译：步态是一种最具前景的生物特征识别技术之一，旨在通过行走模式识别行人。然而，现有方法容易受到混杂因素的影响，导致网络难以聚焦于反映有效行走模式的区域。为了解决步态识别中的这一根本性问题，我们提出了一种生成式反事实干预框架，命名为GaitGCI，该框架由反事实干预学习（CIL）和多样性约束动态卷积（DCDC）组成。CIL通过最大化事实/反事实注意力之间的似然差异来消除混杂因素的影响，而DCDC则自适应地生成样本级别的事实/反事实注意力，以高效感知样本级特性。借助矩阵分解和多样性约束，DCDC保证了模型的高效性和有效性。大量实验表明，所提出的GaitGCI能够：1）有效聚焦于反映步态模式的判别性和可解释性区域；2）具有模型无关性，可嵌入现有模型以几乎零额外代价提升性能；3）在任意场景（实验室环境与野外环境）下均高效达到最先进水平。