TY - GEN
T1 - Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features
AU - Raswa, Farchan Hakim
AU - Kinarta, Indra Yusuf
AU - Pulungan, Reza
AU - Harjoko, Agus
AU - Lee, Chungting
AU - Li, Yung-Hui
AU - Wang, Jia Ching
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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).
AB - 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).
KW - Denoised Wavelet Approach
KW - Fingerprints
KW - Liveness Detection
KW - Spatial and Frequency Feature
UR - http://www.scopus.com/inward/record.url?scp=85142540417&partnerID=8YFLogxK
U2 - 10.1109/ICMLC56445.2022.9941303
DO - 10.1109/ICMLC56445.2022.9941303
M3 - 會議論文篇章
AN - SCOPUS:85142540417
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 103
EP - 108
BT - Proceedings of 2022 International Conference on Machine Learning and Cybernetics, ICMLC 2022
PB - IEEE Computer Society
T2 - 21st International Conference on Machine Learning and Cybernetics, ICMLC 2022
Y2 - 9 September 2022 through 11 September 2022
ER -