GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality (2203.03972v2)

Abstract: Gait is one of the most promising biometrics to identify individuals at a long distance. Although most previous methods have focused on recognizing the silhouettes, several end-to-end methods that extract gait features directly from RGB images perform better. However, we demonstrate that these end-to-end methods may inevitably suffer from the gait-irrelevant noises, i.e., low-level texture and colorful information. Experimentally, we design the cross-domain evaluation to support this view. In this work, we propose a novel end-to-end framework named GaitEdge which can effectively block gait-irrelevant information and release end-to-end training potential. Specifically, GaitEdge synthesizes the output of the pedestrian segmentation network and then feeds it to the subsequent recognition network, where the synthetic silhouettes consist of trainable edges of bodies and fixed interiors to limit the information that the recognition network receives. Besides, GaitAlign for aligning silhouettes is embedded into the GaitEdge without losing differentiability. Experimental results on CASIA-B and our newly built TTG-200 indicate that GaitEdge significantly outperforms the previous methods and provides a more practical end-to-end paradigm. All the source code are available at https://github.com/ShiqiYu/OpenGait.

• The paper identifies that traditional end-to-end gait recognition methods inadvertently capture RGB noise, which undermines cross-domain performance.
• It introduces the GaitEdge framework that synthesizes pedestrian edge information to limit irrelevant details in gait representations.
• The framework's GaitAlign module ensures proper silhouette alignment, yielding a 47.59% mean accuracy improvement in cross-domain tests.

Insights into "GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality"

The paper "GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality" introduces a novel approach for gait recognition, addressing shortcomings in existing methods by enhancing practicality and robustness in cross-domain scenarios. This work primarily critiques and builds upon current end-to-end methodologies that integrate RGB information directly from images, which can inadvertently incorporate gait-irrelevant noise.

Core Contributions

1. Identification of Noise Issues in End-to-end Methods: The authors pinpoint the limitations of prior end-to-end gait recognition methods, emphasizing the unintended extraction of irrelevant noise—such as texture and color from RGB images—within gait representations. This noise undermines the cross-domain robustness of these systems, leading to a performance drop when tested on datasets different from those they were trained on.
2. Proposing the GaitEdge Framework: GaitEdge is introduced as a sophisticated end-to-end framework designed to mitigate noise by synthesizing the output of a pedestrian segmentation network. The approach leverages trainable edges while maintaining fixed interiors within the silhouette representations. This synthesis strategy restricts the recognition network's access to irrelevant information, thus refining its ability to capture genuine gait patterns.
3. Incorporation of GaitAlign Module: The GaitEdge framework integrates a novel GaitAlign module, ensuring proper silhouette alignment without losing differentiability. This module acts as a differentiable size-normalization method and plays a crucial role in preserving the aspect ratio, addressing common challenges presented by varied viewpoint data such as posture misalignment.

Experimental Validation and Results

GaitEdge was evaluated on two datasets: CASIA-B* and the new TTG-200 dataset. The results demonstrated GaitEdge's superior performance, especially in cross-domain evaluations, where existing end-to-end models tend to falter. Notably, GaitEdge achieved a 47.59% mean accuracy in cross-domain testing from TTG-200 to CASIA-B*, outperforming conventional methods.

Theoretical and Practical Implications

From a theoretical standpoint, GaitEdge reinforces the importance of minimizing RGB-informed noise in biometric identifications, especially when scalability and dataset diversity are considerations. Practically, this research suggests a path forward for developing robust biometric systems adaptable to real-world scenarios, where domain shifts are inevitable.

Future Directions

The paper opens avenues for further exploration into noise suppression techniques in biometric systems. Future research may extend GaitEdge principles to other biometric modalities, exploring the integration of similar edge-based filtering approaches. Additionally, this work invites inquiries into more comprehensive cross-domain evaluations, potentially enhancing the generalizability of biometric recognition models.

In summary, the paper presents GaitEdge as a significant advancement in the gait recognition domain, overcoming previous limitations by efficiently managing RGB noise and improving cross-domain performance. Through its innovative design, GaitEdge sets the stage for more adaptable and reliable biometric identification systems in diverse application environments.