  Existing gait recognition methods either directly establish Global Feature
Representation (GFR) from original gait sequences or generate Local Feature
Representation (LFR) from several local parts. However, GFR tends to neglect
local details of human postures as the receptive fields become larger in the
deeper network layers. Although LFR allows the network to focus on the detailed
posture information of each local region, it neglects the relations among
different local parts and thus only exploits limited local information of
several specific regions. To solve these issues, we propose a global-local
based gait recognition network, named GaitGL, to generate more discriminative
feature representations. To be specific, a novel Global and Local Convolutional
Layer (GLCL) is developed to take full advantage of both global visual
information and local region details in each layer. GLCL is a dual-branch
structure that consists of a GFR extractor and a mask-based LFR extractor. GFR
extractor aims to extract contextual information, e.g., the relationship among
various body parts, and the mask-based LFR extractor is presented to exploit
the detailed posture changes of local regions. In addition, we introduce a
novel mask-based strategy to improve the local feature extraction capability.
Specifically, we design pairs of complementary masks to randomly occlude
feature maps, and then train our mask-based LFR extractor on various occluded
feature maps. In this manner, the LFR extractor will learn to fully exploit
local information. Extensive experiments demonstrate that GaitGL achieves
better performance than state-of-the-art gait recognition methods. The average
rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and
63.8%, respectively, significantly outperforming the competing methods. The
proposed method has won the first prize in two competitions: HID 2020 and HID
2021.
