Gait, as one of unique biometric features, has the advantage of being
recognized from a long distance away, can be widely used in public security.
Considering 3D pose estimation is more challenging than 2D pose estimation in
practice , we research on using 2D joints to recognize gait in this paper, and
a new model-based gait recognition method JointsGait is put forward to extract
gait information from 2D human body joints. Appearance-based gait recognition
algorithms are prevalent before. However, appearance features suffer from
external factors which can cause drastic appearance variations, e.g. clothing.
Unlike previous approaches, JointsGait firstly extracted spatio-temporal
features from 2D joints using gait graph convolutional networks, which are less
interfered by external factors. Secondly, Joints Relationship Pyramid Mapping
(JRPM) are proposed to map spatio-temporal gait features into a discriminative
feature space with biological advantages according to the relationship of human
joints when people are walking at various scales. Finally, we design a fusion
loss strategy to help the joints features to be insensitive to cross-view. Our
method is evaluated on two large datasets, Kinect Gait Biometry Dataset and
CASIA-B. On Kinect Gait Biometry Dataset database, JointsGait only uses
corresponding 2D coordinates of joints, but achieves satisfactory recognition
accuracy compared with those model-based algorithms using 3D joints. On CASIA-B
database, the proposed method greatly outperforms advanced model-based methods
in all walking conditions, even performs superior to state-of-art
appearance-based methods when clothing seriously affect people's appearance.
The experimental results demonstrate that JointsGait achieves the state-of-art
performance despite the low dimensional feature (2D body joints) and is less
affected by the view variations and clothing variation.

本研究提出了一种新的基于 2D 人体关节点的步态识别方法 JointsGait，该方法首先使用步态图卷积网络从 2D 关节点提取时空特征，然后通过关节点间的生物学关系将其映射到判别特征空间中。最后，采用融合损失策略来提高各关节点的特征对视角及衣物等干扰项的鲁棒性。实验结果表明，JointsGait 方法具有 2D 关节点特征维度低、少受视角变化及衣物遮挡等因素影响的特点，且在 Kinect Gait Biometry Dataset 数据库和 CASIA-B 数据库上的步态识别准确率都明显高于其他已有模型。

JointsGait: 基于步态图卷积网络和关节关系金字塔映射的基于模型的步态识别方法

JointsGait:A model-based Gait Recognition Method based on Gait Graph  Convolutional Networks and Joints Relationship Pyramid Mapping

The task of three-dimensional (3D) human pose estimation from a single image
can be divided into two parts: (1) Two-dimensional (2D) human joint detection
from the image and (2) estimating a 3D pose from the 2D joints. Herein, we
focus on the second part, i.e., a 3D pose estimation from 2D joint locations.
The problem with existing methods is that they require either (1) a 3D pose
dataset or (2) 2D joint locations in consecutive frames taken from a video
sequence. We aim to solve these problems. For the first time, we propose a
method that learns a 3D human pose without any 3D datasets. Our method can
predict a 3D pose from 2D joint locations in a single image. Our system is
based on the generative adversarial networks, and the networks are trained in
an unsupervised manner. Our primary idea is that, if the network can predict a
3D human pose correctly, the 3D pose that is projected onto a 2D plane should
not collapse even if it is rotated perpendicularly. We evaluated the
performance of our method using Human3.6M and the MPII dataset and showed that
our network can predict a 3D pose well even if the 3D dataset is not available
during training.

本文提出了一种使用生成对抗网络进行无监督学习的方法，能够从单张图片的 2D 关节点位置预测出 3D 人体姿态，无需使用 3D 数据集，而且即使在训练时缺乏数据，该方法也能很好地预测 3D 姿态。