Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features

Farchan Hakim Raswa, Indra Yusuf Kinarta, Reza Pulungan, Agus Harjoko, Chungting Lee, Yung-Hui Li, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)

摘要

Fingerprint has a competent level of uniqueness because the various features can form different patterns in humans. It is a verification requirement in various aspects, such as mobile phone, banking accounts, attendance, etc. One of the preventive measures in maintaining performance is liveness detection. We deep exploited the handcrafted method to achieve adequate performance. To encapsulate the noise possibility, we added the Bayes shrink-wavelet transform as the noise removal. So, the noise obtained in the fingerprint image can be minimized but keep the quality of the fingerprint image is in good condition. Then, we conjugated the spatial and frequency domain in pixel neighborhood distribution using the local binary pattern (LBP) and local phase quantization (LPQ) feature. Finally, we mapped the learning stage using a prominent classifier, i.e., a support vector machine (SVM). Our experiment was evaluated with LivDet 2015 dataset. The proposed method has achieved sustainable results regarding average error rate (AER).

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主出版物標題Proceedings of 2022 International Conference on Machine Learning and Cybernetics, ICMLC 2022
發行者IEEE Computer Society
頁面103-108
頁數6
ISBN(電子)9781665488327
DOIs
出版狀態已出版 - 2022
事件21st International Conference on Machine Learning and Cybernetics, ICMLC 2022 - Toyama, Japan
持續時間: 9 9月 202211 9月 2022

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
2022-September
ISSN(列印)2160-133X
ISSN(電子)2160-1348

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???event.eventtypes.event.conference???21st International Conference on Machine Learning and Cybernetics, ICMLC 2022
國家/地區Japan
城市Toyama
期間9/09/2211/09/22

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