  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.
