Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine‐tuning techniques. This article proposes a color‐mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA‐B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed‐view, crossview, and multiview compared with the recent methodologies.

中文翻译：

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基于鲁棒相似性的深度暹罗卷积神经网络，可跨视图进行步态识别

步态识别已被认为是用于识别人类步行行为的新兴生物识别技术。本文解决的主要挑战是由协变量因素（例如衣服，携带条件和视角变化）引起的显着变化，这将不希望地影响步态的识别性能。近年来，深度学习技术已经基于分类对各种挑战性问题产生了惊人的性能准确性。由于现实世界中的大量数据，卷积神经网络将逼近模型中的复杂非线性函数，以开发用于步态识别的广义深度卷积神经网络（DCNN）体系结构。DCNN可以使用或不使用任何数据增强和微调技术来处理相对较大的多视图数据集。本文提出了一种颜色映射的轮廓步态图像作为步态特征，以解决因辅因子和跨步态识别引起的变化。我们还比较了用于步态模板生成的各种边缘检测算法，并从其中选择了最佳算法。我们工作考虑的数据库包括使用最广泛的CASIA-B数据集和OULP数据库。我们的实验表明，与最近的方法相比，固定视野，交叉视野和多视野的步态识别有了显着改善。我们工作考虑的数据库包括使用最广泛的CASIA-B数据集和OULP数据库。我们的实验表明，与最近的方法相比，固定视野，交叉视野和多视野的步态识别有了显着改善。我们工作考虑的数据库包括使用最广泛的CASIA-B数据集和OULP数据库。我们的实验表明，与最近的方法相比，固定视野，交叉视野和多视野的步态识别有了显着改善。