Gait recognition, which aims at identifying individuals by their walking
patterns, has achieved great success based on silhouette. The binary silhouette
sequence encodes the walking pattern within the sparse boundary representation.
Therefore, most pixels in the silhouette are under-sensitive to the walking
pattern since the sparse boundary lacks dense spatial-temporal information,
which is suitable to be represented with dense texture. To enhance the
sensitivity to the walking pattern while maintaining the robustness of
recognition, we present a Complementary Learning with neural Architecture
Search (CLASH) framework, consisting of walking pattern sensitive gait
descriptor named dense spatial-temporal field (DSTF) and neural architecture
search based complementary learning (NCL). Specifically, DSTF transforms the
representation from the sparse binary boundary into the dense distance-based
texture, which is sensitive to the walking pattern at the pixel level. Further,
NCL presents a task-specific search space for complementary learning, which
mutually complements the sensitivity of DSTF and the robustness of the
silhouette to represent the walking pattern effectively. Extensive experiments
demonstrate the effectiveness of the proposed methods under both in-the-lab and
in-the-wild scenarios. On CASIA-B, we achieve rank-1 accuracy of 98.8%, 96.5%,
and 89.3% under three conditions. On OU-MVLP, we achieve rank-1 accuracy of
91.9%. Under the latest in-the-wild datasets, we outperform the latest
silhouette-based methods by 16.3% and 19.7% on Gait3D and GREW, respectively.

通过使用稠密时空场（DSTF）和基于神经架构搜索（NCL）的互补学习，提出了一种对步态进行识别的框架（CLASH），它能够增强对步态模式的敏感性，同时保持识别的鲁棒性，并在各种情景下取得了良好的效果。

CLASH：用神经架构搜索进行步态识别的互补学习

CLASH: Complementary Learning with Neural Architecture Search for Gait  Recognition

Gait recognition is a promising biometric technology for identification due
to its non-invasiveness and long-distance. However, external variations such as
clothing changes and viewpoint differences pose significant challenges to gait
recognition. Silhouette-based methods preserve body shape but neglect internal
structure information, while skeleton-based methods preserve structure
information but omit appearance. To fully exploit the complementary nature of
the two modalities, a novel triple branch gait recognition framework, TriGait,
is proposed in this paper. It effectively integrates features from the skeleton
and silhouette data in a hybrid fusion manner, including a two-stream network
to extract static and motion features from appearance, a simple yet effective
module named JSA-TC to capture dependencies between all joints, and a third
branch for cross-modal learning by aligning and fusing low-level features of
two modalities. Experimental results demonstrate the superiority and
effectiveness of TriGait for gait recognition. The proposed method achieves a
mean rank-1 accuracy of 96.0% over all conditions on CASIA-B dataset and 94.3%
accuracy for CL, significantly outperforming all the state-of-the-art methods.
The source code will be available at this https URL

本文介绍了一种新的三支路人体步态识别框架（TriGait），它有效地融合了骨架和轮廓数据的特征，包括从外观提取静态和动态特征的两流网络，通过捕捉所有关节之间的依赖关系的简单而有效的 JSA-TC 模块，以及通过对两种模态的低级特征进行对齐和融合的第三支路用于交叉模态学习。实验证据表明 TriGait 在人体步态识别方面具有卓越的优越性和有效性。在 CASIA-B 数据集的所有条件下，所提方法的平均排名准确率为 96.0％，对 CL 数据集的准确率为 94.3％，明显优于所有现有技术方法。源代码将在此 URL 提供。