The essential human gait parameters are briefly reviewed, followed by a detailed review of
the state of the art in deep learning for the human gait analysis. The modalities for capturing …

Gait recognition has emerged as an attractive biometric technology for the identification of
people by analysing the way they walk. However, one of the main challenges of the …

As a unique biometric feature that can be recognized at a distance, gait has broad
applications in crime prevention, forensic identification and social security. To portray a gait …

Learning discriminative representations for unseen person images is critical for person re-
identification (ReID). Most of the current approaches learn deep representations in …

Gait, the walking pattern of individuals, is one of the most important biometrics modalities.
Most of the existing gait recognition methods take silhouettes or articulated body models as …

Person re-identification (re-id), the process of matching pedestrian images across different
camera views, is an important task in visual surveillance. Substantial development of re-id …

Body joints, directly obtained from a pose estimation model, have proven effective for action
recognition. Existing works focus on analyzing the dynamics of human joints. However …

Recently, deep learning-based cross-view gait recognition has become popular owing to the
strong capacity of convolutional neural networks (CNNs). Current deep learning methods …

This paper gives a comprehensive study on gait biometrics via a joint CNN-based method.
Gait is a kind of behavioral biometric feature with unique advantages, eg, long-distance …

Gait recognition is one of the most important techniques for human identification at a
distance. Most current gait recognition frameworks consist of several separate steps …