Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features
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 dete...
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2022
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Online Access: | https://repository.ugm.ac.id/281950/1/Kinarta_MIPA.pdf https://repository.ugm.ac.id/281950/ https://ieeexplore.ieee.org/document/9941303 |
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id-ugm-repo.2819502023-11-15T07:27:52Z https://repository.ugm.ac.id/281950/ Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features RASWA, FARCHAN HAKIM KINARTA, INDRA YUSUF PULUNGAN, REZA HARJOKO, AGUS LEE, CHUNG-TING LI, YUNG-HUI WANG, JIA-CHING Information and Computing Sciences Mathematics and Applied Sciences 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). 2022-11-11 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/281950/1/Kinarta_MIPA.pdf RASWA, FARCHAN HAKIM and KINARTA, INDRA YUSUF and PULUNGAN, REZA and HARJOKO, AGUS and LEE, CHUNG-TING and LI, YUNG-HUI and WANG, JIA-CHING (2022) Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features. In: 21st International Conference on Machine Learning and Cybernetics, ICMLC 2022, 9-11 September 2022, Japan. https://ieeexplore.ieee.org/document/9941303 |
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Information and Computing Sciences Mathematics and Applied Sciences RASWA, FARCHAN HAKIM KINARTA, INDRA YUSUF PULUNGAN, REZA HARJOKO, AGUS LEE, CHUNG-TING LI, YUNG-HUI WANG, JIA-CHING Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
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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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Conference or Workshop Item PeerReviewed |
author |
RASWA, FARCHAN HAKIM KINARTA, INDRA YUSUF PULUNGAN, REZA HARJOKO, AGUS LEE, CHUNG-TING LI, YUNG-HUI WANG, JIA-CHING |
author_facet |
RASWA, FARCHAN HAKIM KINARTA, INDRA YUSUF PULUNGAN, REZA HARJOKO, AGUS LEE, CHUNG-TING LI, YUNG-HUI WANG, JIA-CHING |
author_sort |
RASWA, FARCHAN HAKIM |
title |
Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
title_short |
Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
title_full |
Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
title_fullStr |
Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
title_full_unstemmed |
Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features |
title_sort |
fingerprint liveness detection using denoised-bayes shrink wavelet and aggregated local spatial and frequency features |
publishDate |
2022 |
url |
https://repository.ugm.ac.id/281950/1/Kinarta_MIPA.pdf https://repository.ugm.ac.id/281950/ https://ieeexplore.ieee.org/document/9941303 |
_version_ |
1783956257681440768 |