GaitGCN plus plus : Improving GCN-based gait recognition with part-wise attention and DropGraph

Gait recognition is becoming one of the promising methods for biometric authentication owing to its self-effacing nature. Contemporary approaches of joint position-based gait recognition generally model gait features using spatio-temporal graphs which are often prone to overfitting. To incorporate long-range relationships among joints, these methods utilize multi-scale operators. However, they fail to provide equal importance to all joint combinations resulting in an incomplete realization of long-range relation-ships between joints and important body parts. Furthermore, only considering joint coordinates may fail to capture discriminatory information provided by the bone structures and motion. In this work, a novel multi-scale graph convolution approach, namely 'GaitGCN++', is proposed, which utilizes joint and bone information from individual frames and joint-motion data from consecutive frames providing a compre-hensive understanding of gait. An efficient hop-extraction technique is utilized to understand the rela-tionship between closer and further joints while avoiding redundant dependencies. Additionally, traditional graph convolution is enhanced by leveraging the 'DropGraph' regularization technique to avoid overfitting and the 'Part-wise Attention' to identify the most important body parts over the gait sequence. On the benchmark gait recognition dataset CASIA-B and GREW, we outperform the state-of -the-art in diversified and challenging scenarios.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).