In this paper, we propose a novel network structure GaitNet, which treats gait as a set of
disordered sequences, compares different sequences to extract features, and introduces …

Gait recognition aims at identifying different people by the walking patterns, which can be
conducted at a long distance without the cooperation of subjects. A key challenge for gait …

In this paper, we propose a gait recognition algorithm based on multi-level pooling residual
network to address the problem that traditional gait recognition algorithms have reduced …

Human identification plays a significant role in ensuring social security. However, face-
based and appearance-based retrieval methods are not effective in monitoring due to the …

Gait recognition has many advantages, such as non-invasive and easy to recognize from a
long distance. It has a broad application prospect in the field of human identification …

Gait recognition is a biometric approach used to identify people based on their walking
patterns at long distances and low resolutions. Most advanced gait recognition methods …

In order to make better use of the temporal information of gait data and improve the accuracy
of gait recognition. In this paper, we propose a multi-grained feature aggregate network …

Gait as a biometric feature is widely used for human identification, and gait recognition has
recently become a significant research problem. According to a small amount of labeled …

Gait recognition, an essential branch of biometric identification, uses walking patterns to
identify individuals. Despite its effectiveness, gait recognition faces challenges such as …

At present, many studies have shown that partitioning the gait sequence and its feature map
can improve the accuracy of gait recognition. However, most models just cut the feature map …