GaitGCI: Generative Counterfactual Intervention for Gait Recognition
Huanzhang Dou Pengyi Zhang Wei Su Yunlong Yu Yining Lin Xi Li
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).