IEICE TRANSACTIONS on Information
Weber Centralized Binary Fusion Descriptor for Fingerprint Liveness Detection
Summary :
In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1422-1425
- Publication Date
- 2019/07/01
- Publicized
- 2019/04/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8044
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Asera WAYNE ASERA
Kumamoto University
Masayoshi ARITSUGI
Kumamoto University
Keyword
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Asera WAYNE ASERA, Masayoshi ARITSUGI, "Weber Centralized Binary Fusion Descriptor for Fingerprint Liveness Detection" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1422-1425, July 2019, doi: 10.1587/transinf.2019EDL8044.
Abstract: In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8044/_p
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@ARTICLE{e102-d_7_1422,
author={Asera WAYNE ASERA, Masayoshi ARITSUGI, },
journal={IEICE TRANSACTIONS on Information},
title={Weber Centralized Binary Fusion Descriptor for Fingerprint Liveness Detection},
year={2019},
volume={E102-D},
number={7},
pages={1422-1425},
abstract={In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.},
keywords={},
doi={10.1587/transinf.2019EDL8044},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Weber Centralized Binary Fusion Descriptor for Fingerprint Liveness Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1422
EP - 1425
AU - Asera WAYNE ASERA
AU - Masayoshi ARITSUGI
PY - 2019
DO - 10.1587/transinf.2019EDL8044
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.
ER -
English
