Adaptive thresholding pattern for fingerprint forgery detection

TL;DR

This paper introduces an adaptive thresholding pattern for fingerprint forgery detection that enhances resistance to distortions like noise and missing data, using wavelet transform features and SVM classification.

Contribution

It proposes a novel adaptive thresholding algorithm combined with wavelet-based features and SVM, improving robustness against various distortions in fingerprint forgery detection.

Findings

Outperforms existing methods by 8% in accuracy with 90% pixel missing

Achieves 5% higher accuracy in block missing scenarios of 70x70

Demonstrates improved resistance to environmental and malicious distortions

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 70x70 , respectively. This highlights the novelty approach in addressing such challenges.