GMSN: An efficient multi-scale feature extraction network for gait recognition

Abstract

In current gait recognition methods, researchers predominantly focus on gait information at specific spatial scales, with a tendency to overlook information variances across different scales. Additionally, from observation, we found variations in the spatiotemporal information offered by different human body parts. To address these issues, we present a Multi-scale Network for Gait Recognition (GMSN), which aims to highlight key body parts in a more discriminative gait representation. GMSN consists of two key modules: the Multi-scale Feature Extractor (MSFE) and the Part-based Horizontal Mapping (PHM). MSFE employs multi-scale parallel convolutional networks to comprehensively learn features across different scales, capturing both local details and global information for an enriched representation of gait. Meanwhile, PHM focuses on enhancing the learning of crucial body parts that provide clear contours and movement patterns. Experiments on three public datasets demonstrate that our approach attains state-of-the-art recognition accuracy. On the CASIA-B dataset, our model achieves rank-1 accuracies of 98.2 %, 96.0 %, and 87.0 % under normal walking, bag-carrying, and coat-wearing conditions, respectively. On the OU-MVLP and GREW datasets, it achieves a rank-1 accuracy of 90.4 % and 50.6 %, respectively. Also, it can achieve relatively stable results when adding square occlusions to the test samples.