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. A dataset of 5,743 bonafide and 4,912 PA images of 12 different materials is used to evaluate a state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE visualization and clustering of material characteristics to identify a representative set of PA materials that cover most of PA feature space. We observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh, Gelatin, Latex Body Paint and Monster Liquid Latex provide a good representative set that should be included in training to achieve generalization of PAD. We also propose an optimized Android app of Fingerprint Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4) without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR = 0.2%) which can make a PA prediction in less than 300ms.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
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 a…
Representation LearningStyle TransferDeep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?
The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biome…
fingerprint verificationSaliency-Guided Training for Fingerprint Presentation Attack Detection
Saliency-guided training, which directs model learning to important regions of images, has demonstrated generalization improvements across various biometric presentation attack detection (PAD) tasks. This paper presents …
LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization
The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). Th…
Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021
Fingerprint authentication systems are highly vulnerable to artificial reproductions of fingerprint, called fingerprint presentation attacks. Detecting presentation attacks is not trivial because attackers refine their r…