Gait recognition has significant potential for remote human identification, but it is easily influenced by identity-unrelated factors such as clothing, carrying conditions, and view angles. Many gait templates have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a gait energy image) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for gait recognition.

步态识别具有远距离人类识别的巨大潜力，但很容易受到与身份无关的因素（例如衣服，携带条件和视角）的影响。已经提出了许多可以有效表示步态特征的步态模板。每个步态模板都有其优势，可以代表不同的重要信息。本文提出了步态模板融合技术，以改进经典的代表性步态模板（例如步态能量图像），该模板代表了对轮廓变化敏感的不完整信息。我们还提出了一种分区方法，以反映每个行人不同身体部位的不同步态习惯。根据人体的不同，融合后的模板可分为三部分（头部，躯干和腿部区域），然后将这三个部分发送到卷积神经网络以学习合并的特征。我们对CASIA-B数据集进行了广泛的经验评估，并将所提出的方法与现有方法进行了比较。结果表明，所提出的步态识别方法具有良好的准确性和鲁棒性。