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首页> 外文期刊>International Journal of Neural Systems >Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory
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Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory

机译：基于框架框架步态能量图像和卷积长短短期记忆的人体步态识别

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摘要

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.
机译：人体步态认可是最有前途的生物识别技术之一，特别是对于从远处的不引人注目的视频监测和人类识别。旨在提高识别率，本文研究了使用深度学习的步态认可，并提出了一种基于卷积长短期记忆（CONC-LSTM）的新方法。首先，我们介绍了步态能量图像的变化，即逐帧GEI（FF-GEI），以扩展可用步态能量图像（GEI）数据的体积，并放松现有步态识别所需的步态循环分割的约束方法。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，利用我们人体步态的时间性，我们使用Conv-LSTM设计一种新颖的步态识别模型。最后，基于Casia DataSet B广泛评估所提出的方法，用于跨视图步态识别，此外，采用OU-ISIR大型人口数据集来验证其泛化能力。我们的实验结果表明，所提出的方法基于这两个数据集优于其他算法。结果表明，建议的FF-GEI模型使用CONC-LSTM与新的步态表示，可以有效解决与跨视图步态识别有关的问题。

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