Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and carrying conditions that affect the human gait. In this work, a new approach is proposed for the HGR that contains five major steps. In the first step, the video data is converted into image frames. In the second step, RGB to GRAY conversion is carried out. After that, a two-stream network is designed by using a 55-layer CNN model called CNN-55 trained on CIFAR-100. The CNN-55 is designed from scratch and trained on the CIFAR-100 dataset by selecting hyperparameters. This pre-trained CNN-55 is used to build a two-stream network. In Stream-1 the optical flow frames are obtained by Horn and Schunk algorithm. These frames are fed into a CNN-55 to extract temporal features. In Stream-2 the GRAY frames are fed to the CNN-55 model for extraction of spatial features. After that, both vectors are serially fused. In the fourth step, the fused feature vector is fed into the Genetic Algorithm for optimization. Finally, the feature vector is fed into the One-Versus-All SVM classifier for recognition. The system is tested on all CASIA-B angles such as 00, 18, 36, 54, 72, 90, 108, 126, 144, 162, and 180 which provides accuracy of 97.10%, 96.80%, 94.60%, 98.0%, 98.30%, 96.80%, 97.60, 96.90%, 99.60%, 96.80%, and 97.60%, respectively. The proposed method produces better outcomes compared to recent techniques.

中文翻译：

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使用两流神经网络以及空间和时间特征进行人类步态识别

人类步态识别（HGR）是一种生物识别策略，广泛用于通过行走模式来识别个体。影响人类步态的关键因素有角度变化、服装变化、足部阴影、携带条件等。在这项工作中，为 HGR 提出了一种新方法，其中包含五个主要步骤。第一步，将视频数据转换为图像帧。第二步，进行RGB到GRAY的转换。之后，使用在 CIFAR-100 上训练的 55 层 CNN 模型 CNN-55 设计了一个双流网络。 CNN-55 是从头开始设计的，并通过选择超参数在 CIFAR-100 数据集上进行训练。这个预训练的 CNN-55 用于构建双流网络。 Stream-1中的光流帧是通过Horn和Schunk算法获得的。这些帧被输入 CNN-55 以提取时间特征。在 Stream-2 中，灰色帧被馈送到 CNN-55 模型以提取空间特征。之后，两个向量被连续融合。第四步，将融合后的特征向量输入遗传算法进行优化。最后，将特征向量输入 One-Versus-All SVM 分类器进行识别。该系统在所有 CASIA-B 角度（例如 00、18、36、54、72、90、108、126、144、162 和 180）上进行了测试，提供了 97.10%、96.80%、94.60%、98.0% 的精度，分别为 98.30%、96.80%、97.60、96.90%、99.60%、96.80% 和 97.60%。与最近的技术相比，所提出的方法产生了更好的结果。