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Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems

Pan, Shi, Deravi, Farzin (2019) Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems. In: 2019 Eighth International Conference on Emerging Security Technologies (EST). . pp. 1-6. IEEE ISBN 978-1-72815-546-3. (doi:10.1109/EST.2019.8806220) (KAR id:76082)

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Official URL
https://doi.org/10.1109/EST.2019.8806220

Abstract

Spatio-temporal information is valuable as a discriminative cue for presentation attack detection, where the temporal texture changes and fine-grained motions (such as eye blinking) can be indicative of some types of spoofing attacks. In this paper, we propose a novel spatio-temporal feature, based on motion history, which can offer an efficient way to encapsulate temporal texture changes. Patterns of motion history are used as primary features followed by secondary feature extraction using Local Binary Patterns and Convolutional Neural Networks, and evaluated using the Replay Attack and CASIA-FASD datasets, demonstrating the effectiveness of the proposed approach.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EST.2019.8806220
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Farzin Deravi
Date Deposited: 02 Sep 2019 09:16 UTC
Last Modified: 16 Feb 2021 14:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/76082 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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