National Institute of Technology Rourkela, Rourkela 769008, India.
Sensors (Basel). 2022 May 24;22(11):3968. doi: 10.3390/s22113968.
Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.
基于智能手机的步态识别已被认为是一种独特且有前途的生物识别技术。它集成了多个传感器来采集人在行走时的惯性数据。然而，由于步态序列的变化，如携带负载、穿着类型、鞋类类型等，捕获的数据可能会受到多种协变量因素的影响。最近的步态识别方法要么基于全局特征，要么基于局部特征，因此无法处理这些基于协变量的特征。为了解决这些问题，设计了一种新颖的加权多尺度卷积神经网络（WMsCNN）架构，用于提取局部到全局特征，以提高识别精度。具体来说，提出了一个权重更新子网络（Ws），用于增加或减少特征的权重，以反映它们对最终分类任务的贡献。因此，通过权重更新技术，这些特征对协变量因素的敏感性降低。然后，这些特征被输入到融合模块中，用于生成全局特征进行整体分类。在四个不同的基准数据集上进行了广泛的实验，所提出模型的演示结果优于其他最先进的深度学习方法。
