Decomposing Identity and View for Cross-View Gait Recognition

Zhai, Xinliang; Ben, Xianye; Liu, Chang; Xie, Tingxuan

doi:10.1109/icme52920.2022.9859981

Cited by 3 publications

(1 citation statement)

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“…Hu [30] et al integrated a multi-branch residual structure into the CNNs, which facilitated the model to make full use of the more representative feature information of the input image. Zhai [31] et al used an autoencoder (AE) to separate identity features and view features from GEI for coding recombination and reconstructed the GEI for identity recognition. Wang [32] et al constrained the generator using multiple loss functions on the basis of GANs to improve the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Cross-View Gait Recognition Model Combining Multi-Scale Feature Residual Structure and Self-Attention Mechanism

Wang,

Guo,

2023

IEEE Access

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In the cross-view condition, the gait recognition rate caused by the vastly different gait silhouette maps is substantially reduced. To improve the accuracy of gait recognition under cross-view conditions, this paper proposes a cross-view gait recognition network model combining multi-scale feature residual module (MFRM) and self-attention (SA) mechanism based on Generative Adversarial Network (GAN). First, the local and global feature information in the input gait energy image is fully extracted using the MFRM. Then, the SA mechanism module is used to adjust the information of channel dimensions and capture the association between feature information and is introduced into both the generator and discriminator. Next, the model is trained using a two-channel network training strategy to avoid the pattern collapse problem during training. Finally, the generator and discriminator are optimized to improve the quality of the generated gait images. This paper conducts experiments using the CASIA-B and OU-MVLP public datasets. The experiments demonstrate that the MFRM can better obtain the local and global feature information of the images. The SA mechanism module can effectively establish global dependencies between features, so that the generated gait images have clearer and richer detail information. The average Rank-1 recognition accuracies of the results in this paper reach 91.1% and 97.8% on the two datasets respectively, which are both better than the current commonly used algorithms, indicating that the network model in this paper can well improve the gait recognition accuracy across perspectives.INDEX TERMS cross-view, gait recognition, residual module, self-attention mechanism, two-channel network.

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Section: Introductionmentioning

confidence: 99%