Gait recognition captures gait patterns from the walking sequence of an individual for identification. Most existing gait recognition methods learn features from silhouettes or skeletons for the robustness to clothing, carrying, and other exterior factors. The combination of the two data modalities, however, is not fully exploited. Previous multimodal gait recognition methods mainly employ the skeleton to assist the local feature extraction where the intrinsic discrimination of the skeleton data is ignored. This paper proposes a simple yet effective Bimodal Fusion (BiFusion) network which mines discriminative gait patterns in skeletons and integrates with silhouette representations to learn rich features for identification. Particularly, the inherent hierarchical semantics of body joints in a skeleton is leveraged to design a novel Multi-Scale Gait Graph (MSGG) network for the feature extraction of skeletons. Extensive experiments on CASIA-B and OUMVLP demonstrate both the superiority of the proposed MSGG network in modeling skeletons and the effectiveness of the bimodal fusion for gait recognition. Under the most challenging condition of walking in different clothes on CASIA-B, our method achieves the rank-1 accuracy of 92.1%.
翻译:Gait 识别方法主要使用骨骼来帮助本地特征提取,因为骨骼数据固有的差别被忽略。本文建议建立一个简单而有效的双式聚合(BiFusion)网络,在骨骼中埋设有歧视性的步态模式,并结合双形图示以学习丰富的特征进行识别。特别是,骨骼内接合体固有的等级语义被利用来设计一个新的多层盖图(MSGG)网络,以提取骨骼特征。关于CASIA-B和UMVLP的广泛实验表明,拟议的MSGG网络在骨架建模方面具有优势,双式混凝土识别的有效性。在CSIA-B不同服装行走的最困难条件下,我们的方法达到了92%的等级-1准确性。