Mr. Ying Li Profile

Ying Li

at Beijing Jiaotong Univ

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SPIE Journal Paper | 23 January 2018

View-invariant gait recognition method by three-dimensional convolutional neural network

Weiwei Xing, Ying Li, Shunli Zhang

JEI, Vol. 27, Issue 01, 013010, (January 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.1.013010

KEYWORDS: Gait analysis, Video, 3D modeling, Lithium, Convolution, Convolutional neural networks, Data modeling, Performance modeling, Optical flow, Image analysis

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Gait as an important biometric feature can identify a human at a long distance. View change is one of the most challenging factors for gait recognition. To address the cross view issues in gait recognition, we propose a view-invariant gait recognition method by three-dimensional (3-D) convolutional neural network. First, 3-D convolutional neural network (3DCNN) is introduced to learn view-invariant feature, which can capture the spatial information and temporal information simultaneously on normalized silhouette sequences. Second, a network training method based on cross-domain transfer learning is proposed to solve the problem of the limited gait training samples. We choose the C3D as the basic model, which is pretrained on the Sports-1M and then fine-tune C3D model to adapt gait recognition. In the recognition stage, we use the fine-tuned model to extract gait features and use Euclidean distance to measure the similarity of gait sequences. Sufficient experiments are carried out on the CASIA-B dataset and the experimental results demonstrate that our method outperforms many other methods.

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