Fingerprint Liveness Detection Using Handcrafted Feature Descriptors and Neural Network

Zhi Sheng Chen, Reza Pulungan, Yung-Hui Li, Farchan Hakim Raswa, Agus Harjoko, Jia Ching Wang, Indra Yusuf Kinarta, Chung Ting Lee

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Fingerprint recognition is commonly used to verify a user's identity. However, the fingerprint recognition systems in use today can be vulnerable to attacks. For example, some artificial fingerprints can spoof fingerprint recognition systems and use identity information to obtain personal information. For security reasons, fingerprint liveness detectors should be robust to attacks using various materials. As a result, we propose a fingerprint recognition method that uses multi-radius local binary pattern (Multi-radius LBP) and local phase quantization (LPQ) as local texture descriptors and wavelet transforms to remove noise. Classification results are obtained using a multi-layer perceptron (MLP) classifier. This study confirms that the proposed method is useful to improve the average fingerprint liveness detection tasks. On LivDet 2015 dataset, the proposed method achieves 6.73% Average Error Rate(AER).

原文???core.languages.en_GB???
主出版物標題GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics
發行者Institute of Electrical and Electronics Engineers Inc.
頁面619-621
頁數3
ISBN(電子)9781665492324
DOIs
出版狀態已出版 - 2022
事件11th IEEE Global Conference on Consumer Electronics, GCCE 2022 - Osaka, Japan
持續時間: 18 10月 202221 10月 2022

出版系列

名字GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics

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???event.eventtypes.event.conference???11th IEEE Global Conference on Consumer Electronics, GCCE 2022
國家/地區Japan
城市Osaka
期間18/10/2221/10/22

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