Human gait is a proven biometric trait with applications in security for authentication and
disease diagnosis. However, it is one-sided to express and interpret gait data from a single …

This paper proposes a novel spatial-temporal deep neural network (STDNN) that is applied
to multi-view gait recognition. The STDNN comprises a temporal feature network (TFN) and …

Abstract Systems based on physiological biometrics are ubiquitous but requires subject
cooperation or high resolution to capture. Gait recognition is a great avenue for identification …

Gait recognition, the task of identifying an individual based on their unique walking style, can
be difficult because walking styles can be influenced by external factors such as clothing …

As a biological feature that can be recognized from a distance, gait has a wide range of
applications such as crime prevention, judicial identification, and social security. However …

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 …

Gait recognition has made significant progress recently. However, most of existing methods
utilize complicated neural networks, which lead to high computation cost. In this paper, a …

Gait recognition which is one of the most important and effective biometric technologies has
a significant advantage in long-distance recognition systems. For existing gait recognition …

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 …

Some recent studies use a convolutional neural network (CNN) or long short-term memory
(LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high …