Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
Joshua J. Engelsma,Anil K. Jain +1 more
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TLDR
This work trains multiple generative adversarial networks on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader to approach spoof detection as a one-class classification problem.Abstract:
Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of "live" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively.read more
Citations
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Journal ArticleDOI
Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks
Anjith George,Sébastien Marcel +1 more
TL;DR: A new framework for PAD is proposed using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN) and a novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks.
Journal ArticleDOI
Fingerprint Spoof Detector Generalization
Tarang Chugh,Anil K. Jain +1 more
TL;DR: A style-transfer based wrapper, called Universal Material Generator (UMG), is presented, to improve the generalization performance of any fingerprint spoof (presentation attack) detector against spoofs made from materials not seen during training.
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Fingerprint classification and identification algorithms for criminal investigation: A survey
TL;DR: This survey presents an up-to-date literature evaluation of fingerprint classification algorithms and fingerprint application in the area of criminal investigation and highlights the challenges in the fingerprint analysis.
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Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks
Lazaro J. Gonzalez-Soler,Marta Gomez-Barrero,Leonardo Chang,Airel Pérez-Suárez,Christoph Busch +4 more
TL;DR: In this paper, the authors proposed a new PAD technique based on three image representation approaches combining local and global information of the fingerprint, which can correctly discriminate bona fide from attack presentations in the aforementioned scenarios.
Journal ArticleDOI
Client-specific anomaly detection for face presentation attack detection
TL;DR: It is shown that the use of client-specific information in a one-class anomaly detection formulation improves the performance significantly, and it is demonstrated that the same set of deep convolutional features used for the recognition purposes is effective for face presentation attack detection in the class-specific one- class anomaly detection paradigm.
References
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