Cross-view gait recognition based on multi-scale feature fusion

Home > Archive>Volume 47, Issue 1, 2024 >186-192

Cross-view gait recognition based on multi-scale feature fusion
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                        Zou XueZou Xue
1.School of Data Science and Information Engineering, Guizhou Minzu University，Guiyang 550025, China； 2.Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China
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Tan MianTan Mian
1.School of Data Science and Information Engineering, Guizhou Minzu University，Guiyang 550025, China； 2.Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China
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Yan XiaoboYan Xiaobo
1.School of Data Science and Information Engineering, Guizhou Minzu University，Guiyang 550025, China； 2.Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China
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Wang FeiWang Fei
2.Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China； 3.College of Humanities & Sciences, Guizhou Minzu University，Guiyang 550025, China
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Wang Lin2Wang Lin
Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China
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Affiliation:1.School of Data Science and Information Engineering, Guizhou Minzu University，Guiyang 550025, China; 2.Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University，Guiyang 550025, China; 3.College of Humanities & Sciences, Guizhou Minzu University，Guiyang 550025, China
Clc Number:TP75
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Abstract:

In cross-view gait recognition, it is difficult to extract distinguishable and diverse gait features in the case of clothing occlusion, which leads to the decrease of recognition accuracy. A multi-scale feature fusion network based cross-view gait recognition method is proposed. This method can effectively utilize the complementarity among gait features to obtain gait features with discriminability and diversity, thereby solving the problem of poor discriminability and uniformity caused by clothing occlusion, and thus improving the accuracy of cross-viewing Angle gait recognition. In order to verify the effectiveness of the proposed method, the public data set CASIA-B was used to verify the proposed method. The experimental results show that the proposed method achieves 73.4% recognition performance for the cross-viewing Angle gait recognition problem with occlusion, and 95.5% and 88.0% recognition performance under normal and backpack walking conditions, respectively. In addition, the performance of our method is better than that of other typical gait recognition methods under occluded conditions.

Key words:cross-view gait recognition;multi-scale feature fusion;gait feature;discriminability

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Online: April 24,2024
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