Gait recognition based on dynamic areas convolution

Wanjun Liu; Hui Cai

doi:10.1117/12.2681338

8 June 2023 Gait recognition based on dynamic areas convolution

Wanjun Liu, Hui Cai

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073B (2023) https://doi.org/10.1117/12.2681338
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Gait recognition has achieved high recognition accuracy under normal walking conditions, but in the case of occlusion, the recognition efficiency needs to be improved. To solve this problem, this paper improves the Gaitset network: change the primary feature extraction network into dynamic region-aware convolution, and dynamically generate corresponding filters according to the feature distribution of different regions of the gait image to extract features, so as to enhance the representation ability of convolution; GEI is added to the MGP module to make up for the lack of global information in the deep network. The experimental results on CASIA-B dataset show that the gait recognition rate of this algorithm under NM, BG and CL conditions is 3.4%, 5.0% and 6.9% higher than that of GaitSet algorithm. It is proved that this experiment has certain advantages for gait recognition under occlusion.

Citation Download Citation

Wanjun Liu and Hui Cai "Gait recognition based on dynamic areas convolution", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073B (8 June 2023); https://doi.org/10.1117/12.2681338

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