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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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 83; no. 34; pp. 81665 - 81683
Main Authors:	Farzadpour, Zahra, Azghani, Masoumeh
Format:	Journal Article
Language:	English
Published:	New York Springer US 08-03-2024 Springer Nature B.V
Subjects:	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 Haar wavelet transform Local binary pattern Fingerprint forgery detection Support vector machine Anisotropic diffusion Adaptive thresholding
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Summary:	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.
ISSN:	1573-7721 1380-7501 1573-7721
DOI:	10.1007/s11042-024-18649-3