Share Email Print
cover

Proceedings Paper • new

A new gait energy image based on mask processing for pedestrian gait recognition
Author(s): Zhong Li; Jiulong Xiong; Xiangbin Ye
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Under a more realistic experimental setup, the performance of the existing gait recognition approaches would drop drastically. Because pedestrians are mostly under different and unknown covariate conditions. Thus, the influence caused by changes of clothing and carrying on profile of pedestrians is the main obstacle of gait recognition. In this paper, we propose a new Gait Energy Image based on mask processing (MP-GEI) to reduce the influence of covariate conditions. Firstly, we calculate the Gait Energy Image (GEI) and its synthetic average template which includes the various features of 124 subjects under different covariate conditions from five views (54°, 72°, 90°, 108°, 126°). Secondly, we propose Gait Entropy Image (GEnI) and calculate its synthetic average template (T-GEnI). Thirdly, we calculate the mask representing the dynamic feature areas in T-GEnI by setting the threshold. Finally, we use parts of the mask to remove the irrelevant gait information in GEI. In this work, we explore the performance of MP-GEI with two models based on convolution neural network (CNN), and experiments are carried out on the CASIA Dataset B. Our results demonstrate that the proposed approach achieves better correct classification rate compared with GEI when pedestrians are under different and unknown covariate conditions. In addition, using the pre-trained VGG-16 model to extract deep features for recognition is more effective than fine-tuning the pre-trained VGG-16 model.

Paper Details

Date Published: 27 November 2019
PDF: 9 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212A (27 November 2019); doi: 10.1117/12.2547870
Show Author Affiliations
Zhong Li, National Univ. of Defense Technology (China)
Jiulong Xiong, National Univ. of Defense Technology (China)
Xiangbin Ye, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

© SPIE. Terms of Use
Back to Top