TY - JOUR
T1 - A novel weber local binary descriptor for fingerprint liveness detection
AU - Xia, Zhihua
AU - Yuan, Chengsheng
AU - Lv, Rui
AU - Sun, Xingming
AU - Xiong, Neal N.
AU - Shi, Yun Qing
N1 - Funding Information:
Manuscript received April 24, 2018; revised July 3, 2018 and August 29, 2018; accepted October 3, 2018. Date of publication October 24, 2018; date of current version March 17, 2020. This work was supported in part by the Jiangsu Basic Research Programs-Natural Science Foundation under Grant BK20181407, Grant BK20150925, and Grant BK20151530; in part by the National Natural Science Foundation of China under Grant 61672294; in part by the Six Peak Talent Project of Jiangsu Province under Grant R2016L13; in part by the National Natural Science Foundation of China under Grant 61502242, Grant 61702276, Grant U1536206, Grant U1405254, Grant 61772283, Grant 61602253, Grant 61601236, and Grant 61572258; in part by the National Key Research and Development Program of China under Grant 2018YFB1003205; in part by the NRF under Grant 2016R1D1A1B03933294; in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions Fund; and in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Fund, China. The work of Z. Xia was supported by BK21+ Program from the Ministry of Education of South Korea. This paper was recommended by Associate Editor X. Wang. (Corresponding author: Neal N. Xiong.) Z. Xia is with the Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China, with the School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China, and also with the College of Information and Communication Engineering, Sungkyunkwan University, Seoul 16419, South Korea (e-mail: xia_zhihua@163.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: The local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.
AB - In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: The local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.
KW - Biometrics
KW - Weber's law
KW - digital forensics
KW - fingerprint liveness detection (FLD)
KW - local binary pattern (LBP)
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U2 - 10.1109/TSMC.2018.2874281
DO - 10.1109/TSMC.2018.2874281
M3 - Article
AN - SCOPUS:85055720344
SN - 2168-2216
VL - 50
SP - 1526
EP - 1536
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 4
M1 - 8506619
ER -