Authors:
Yuki Hirose
1
;
Kazuaki Nakamura
2
;
Naoko Nitta
3
and
Noboru Babaguchi
4
Affiliations:
1
Graduate School of Engineering, Osaka University, Suita, Osaka, 565-0871, Japan
;
2
Faculty of Engineering, Tokyo University of Science, Tokyo, 125-8585, Japan
;
3
School of Human Environmental Sciences, Mukogawa Women’s University, Nishinomiya, Hyogo, 663-8558, Japan
;
4
Institute for Datability Science, Osaka University, Suita, Osaka, 565-0871, Japan
Keyword(s):
Gait Recognition, Spoofing Attacks, Master Gait, Masterization, Gait Spoofing, Fake Gait Silhouettes, Multimedia Generation.
Abstract:
Deep learning technologies have improved the performance of biometric systems as well as increased the risk of spoofing attacks against them. So far, lots of spoofing and anti-spoofing methods were proposed for face and voice. However, for gait, there are a limited number of studies focusing on the spoofing risk. To examine the executability of gait spoofing, in this paper, we attempt to generate a sequence of fake gait silhouettes that mimics a certain target person’s walking style only from his/her single photo. A feature vector extracted from such a single photo does not have full information about the target person’s gait characteristics. To complement the information, we update the extracted feature so that it simultaneously contains various people’s characteristics like a wolf sample. Inspired by a wolf sample or also called “master” sample, which can simultaneously pass two or more verification systems like a master key, we call the proposed process “masterization”. After the
masterization, we decode its resultant feature vector to a gait silhouette sequence. In our experiment, the gait recognition accuracy with the generated fake silhouette sequences is increased from 69% to 78% by the masterization, which indicates an unignorable risk of gait spoofing.
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