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Found 9 papers, 0 papers with code
Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach
The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i. e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods.
Fingerprint Spoof Detection: Temporal Analysis of Image Sequence
We utilize the dynamics involved in the imaging of a fingerprint on a touch-based fingerprint reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers.
Universal Material Translator: Towards Spoof Fingerprint Generalization
Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones.
Fingerprint Spoof Generalization
The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75. 24% to 91. 78% @ FDR = 0. 2%.
OCT Fingerprints: Resilience to Presentation Attacks
Optical coherent tomography (OCT) fingerprint technology provides rich depth information, including internal fingerprint (papillary junction) and sweat (eccrine) glands, in addition to imaging any fake layers (presentation attacks) placed over finger skin.
Fingerprint Presentation Attack Detection: Generalization and Efficiency
We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training.
Altered Fingerprints: Detection and Localization
Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS.
Matching Fingerphotos to Slap Fingerprint Images
We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images.
Fingerprint Spoof Buster
Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios.
