基于非对称双路识别网络的步态识别方法

步态作为一种人体躯干、关节、上下肢及各肌群的周期性行为模式,是可用于身份识别过程的一种重要生物特征.针对现有的步态识别方法大都是基于步态轮廓图或者步态能量图提取的全局特征,而忽视了对细粒度步态信息的有效利用的问题,提出了一种包括全局通路和局部通路的非对称双路识别网络.其中全局通路采用三元组损失函数,用于提取步态的全局时...

Novel Gait Recognition Method Based on Asymmetric Two-Path Network

As a periodic behavior pattern of the human torso, joints, lower and upper limbs, gait is an important biological feature that can be used in the identification process. In view of the fact that most of existing gait recognition methods are based on the global features extracted from gait silhouettes or gait energy images and neglect the effective use of fine-grained gait information, an asymmetric two-path identification network including the global path and the local path is proposed. The global path uses a triple loss function to extract the global spatio-temporal features of gait, while the local path uses a cross-entropy loss function to identify significantly different local features in gait. In addition, a novel module named salient local feature detector is added to the local path for recognizing fine-grained gait information effectively. Finally, comparative experiments are conducted on public datasets CASIA-B and OU-ISIR-LP, the results show that, in the cross-view and cross-scenario environment, the proposed method has a significant improvement in recognition accuracy compared to the existing methods.