Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory
Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory
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DOI：
10.1142/s0129065719500278
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发表时间：
2020-01-01
影响因子：
8
通讯作者：
Yan, Wei Qi
中科院分区：
文献类型：
--
作者：
Wang, Xiuhui;Yan, Wei Qi
人体步态识别是最有前途的生物特征识别技术之一，特别是对于非侵入式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，我们提出了一种变化的步态能量图像，即逐帧GEI（ff-GEI），以扩大可用的步态能量图像（GEI）数据的体积和放松现有的步态识别方法所需的步态周期分割的约束。其次，我们通过分析一个人的步态数据的互协方差来证明ff-GEI的有效性。然后，利用我们人类步态的时间性，我们设计了一个新的步态识别模型，使用Conv-LSTM。最后，基于CASIA步态识别数据集B对该方法进行了广泛的评估，并利用OU-ISIR大规模数据集验证了该方法的泛化能力。我们的实验结果表明，该方法优于其他算法的基础上，这两个数据集。实验结果表明，基于Conv-LSTM的ff-GEI模型结合新的步态表示，能够有效解决跨视角步态识别问题。
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.