Associated Spatio-Temporal Capsule Network for Gait Recognition

Abstract

It is a challenging task to identify a person based on her/his gait patterns. State-of-the-art approaches rely on the analysis of temporal or spatial characteristics of gait, and gait recognition is usually performed on single modality data (such as images, skeleton joint coordinates, or force signals). Evidence has shown that using multi-modality data is more conducive to gait research. Therefore, we here establish an automated learning system, with an associated spatio-temporal capsule network (ASTCapsNet) trained on multi-sensor datasets, to analyze multimodal information for gait recognition. Specifically, we first design a low-level feature extractor and a high-level feature extractor for spatio-temporal feature extraction of gait with a novel recurrent memory unit and a relationship layer. Subsequently, a Bayesian model is employed for the decisionmaking of class labels. Extensive experiments on several public datasets (normal and abnormal gait) validate the effectiveness of the proposed ASTCapsNet, compared against several state-ofthe-art methods.

Gait Recognition Based on Deep Learning: A Survey

“…[5,133] Auto Encoder Works by compressing and decompressing features from the input. [85,122,134] Capsule Improve the semantic organization of the outputs from a CNN. [21,119] Deep Belief Networks Encode features and patterns into compressed representations.…”

“…Finally, Zhao et al [134] introduced an automated learning system called Associated Spatio-Temporal Capsule Network (ASTCapsNet). The model was trained over multi-sensor datasets to show that multi-modality data is more conducive to gait recognition.…”