Multiple motion pattern augmentation assisted gait recognition

• Published in: Signal processing Vol. 238; p. 110185
• Main Authors: Huo, Wei, Tang, Jun, Bao, Wenxia, Wang, Ke, Wang, Nian, Liang, Dong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2026
• Subjects: Dynamic motion difference extraction; Fine-grained gait representation; Gait recognition; Motion pattern augmentation; Multi-stage feature aggregation; Multi-stage feature aggregation; Dynamic motion difference extraction; Fine-grained gait representation; Gait recognition; Motion pattern augmentation
• ISSN: 0165-1684
• DOI: 10.1016/j.sigpro.2025.110185

Gait recognition aims to learn the unique walking patterns of different subjects for identity retrieval. Most methods focus on exploiting robust spatio-temporal representations via global–local feature learning or multi-scale temporal modeling. However, they may neglect the dynamic posture change within consecutive frames. In addition, few methods explore useful motion cues by data self-mining manner. In this paper, we propose a novel Motion Pattern Augmentation assisted gait recognition framework named GaitMPA, which explores diverse behavior characteristics from the augmented movement sequences. GaitMPA consists of three components: Motion perception and Fine-grained variation extraction Network (MFNet), Motion Pattern Augmentation (MPA), and Multi-stage Feature Aggregation (MFA). Specifically, we present MFNet to capture dynamic motion difference between neighboring frames by Motion Perception Module (MPM), and extract multi-grained body representation via Fine-grained Variation Extractor (FVE). In MPA, we transform raw sequences into four novel motion patterns to provide distinctive movement traits. Furthermore, MFA is designed to merge the multi-source features of the raw and augmented sequences, and perform multi-stage motion information aggregation. The outputs of MFNet and MFA are fused for gait recognition. Experimental results demonstrate the effectiveness of GaitMPA on five public datasets, including the CASIA-B (in-the-lab), OU-MVLP (in-the-lab), CCPG (cloth-changing), GREW (in-the-wild), and Gait3D (in-the-wild). •We present a novel motion augmentation module to generate diverse gait sequences.•We design a novel gait recognition method, GaitMPA, to learn powerful gait features.•We validate the effectiveness of the proposed method on five public gait datasets.