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Proceedings Paper

Clothing and carrying invariant gait-based gender recognition
Author(s): Taocheng Liu; Xiangbin Ye; Bei Sun
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Paper Abstract

The main obstacle of gait-based gender recognition, compared with other biometrics, is that the influence of changes of clothing, carrying and surface on profile of pedestrians. In this paper, we propose a novel gait representation to reduce the influence of covariate conditions: clothing and carrying invariant area of GEI (Inv-GEI). Firstly, we calculate the GEI and its synthetic common templates that include the various features of female and male under different conditions. Then an improved gait entropy map is proposed to get the mask automatically, containing females and males commonfeatures. To this end, we can use the mask to remove the information that is irrelevant to gait in GEI, and get the gait features that are invariant to condition changes, which is beneficial to gender recognition. This paper explores the performance of Inv-GEI with the state-of-the-art deep convolution model Vgg-16 based on the CASIA B dataset. The experimental results have shown that the proposed method achieves excellent recognition rate under clothing and carrying conditions.

Paper Details

Date Published: 29 October 2018
PDF: 8 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360X (29 October 2018); doi: 10.1117/12.2514908
Show Author Affiliations
Taocheng Liu, National Univ. of Defense Technology (China)
Xiangbin Ye, National Univ. of Defense Technology (China)
Bei Sun, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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