Adaptive thresholding pattern for fingerprint forgery detection
Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerpri...
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| Published in: | Multimedia tools and applications Vol. 83; no. 34; pp. 81665 - 81683 |
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| Abstract | Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size
70
×
70
, respectively. This highlights the novelty approach in addressing such challenges. |
|---|---|
| AbstractList | Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size
70
×
70
, respectively. This highlights the novelty approach in addressing such challenges. Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size 70×70, respectively. This highlights the novelty approach in addressing such challenges. |
| Author | Farzadpour, Zahra Azghani, Masoumeh |
| Author_xml | – sequence: 1 givenname: Zahra surname: Farzadpour fullname: Farzadpour, Zahra organization: Laboratory of Wireless Communication and Signal Processing (WCSP), Faculty of Electrical Engineering, Sahand University of Technology – sequence: 2 givenname: Masoumeh surname: Azghani fullname: Azghani, Masoumeh email: mazghani@sut.ac.ir organization: Laboratory of Wireless Communication and Signal Processing (WCSP), Faculty of Electrical Engineering, Sahand University of Technology |
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| Cites_doi | 10.1007/s11042-021-11254-8 10.1109/NUICONE.2012.6493244 10.1007/s11554-020-01060-0 10.1109/ITCA52113.2020.00090 10.1016/j.neucom.2008.11.003 10.1016/j.future.2010.11.024 10.1088/1757-899X/1042/1/012017 10.1049/iet-bmt.2016.0007 10.1007/s11235-010-9316-0 10.1007/11608288_30 10.1007/978-3-540-74549-5_33 10.1109/BTAS.2017.8272724 10.1007/s11063-020-10407-4 10.1007/s11042-023-15300-5 10.1109/34.56205 10.1007/978-981-19-6142-7_25 10.1109/ICCSP.2017.8286604 10.1109/IPRECON52453.2021.9640976 10.1016/j.patcog.2008.06.012 10.1109/BTAS.2017.8272745 10.1109/LSP.2016.2636158 10.1155/2022/1070405 10.1109/ICEEOT.2016.7755337 10.1109/TIP.2015.2422574 10.32604/cmc.2023.031622 10.1109/TIFS.2023.3251862 10.1007/978-3-540-74549-5_78 10.1109/TSMCC.2005.848192 10.1155/2022/6335201 10.1109/TIFS.2016.2535899 10.1109/CVPR52729.2023.01974 |
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| Keywords | Haar wavelet transform Local binary pattern Fingerprint forgery detection Support vector machine Anisotropic diffusion Adaptive thresholding |
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| SubjectTerms | Adaptive algorithms Adaptive systems Computer Communication Networks Computer Science Contamination Data Structures and Information Theory Diffusion barriers Diffusion layers Fingerprints Forgery Multimedia Information Systems Pixels Special Purpose and Application-Based Systems Spoofing Support vector machines Track 3: Biometrics and HCI Wavelet transforms |
| Title | Adaptive thresholding pattern for fingerprint forgery detection |
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