Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person’s gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

中文翻译：

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基于逐帧步态能量图像和卷积长短期记忆的人体步态识别

人体步态识别是最有前途的生物识别技术之一，尤其适用于不显眼的视频监控和远距离人体识别。为了提高识别率，本文研究了使用深度学习的步态识别，并提出了一种基于卷积长短期记忆（Conv-LSTM）的新方法。首先，我们提出了一种步态能量图像的变体，即逐帧 GEI (ff-GEI)，以扩大可用步态能量图像 (GEI) 数据的数量，并放宽现有步态识别所需的步态周期分割的约束。方法。其次，我们通过分析一个人的步态数据的交叉协方差来证明 ff-GEI 的有效性。然后，利用我们人类步态的时间性，我们使用 Conv-LSTM 设计了一个新颖的步态识别模型。最后，该方法基于 CASIA 数据集 B 进行了广泛的评估，用于跨视图步态识别，并采用 OU-ISIR 大种群数据集验证其泛化能力。我们的实验结果表明，所提出的方法优于基于这两个数据集的其他算法。结果表明，所提出的使用 Conv-LSTM 的 ff-GEI 模型，再加上新的步态表示，可以有效地解决与跨视图步态识别相关的问题。