On the Generalisation Capabilities of Fingerprint Presentation Attack Detection Methods in the Short Wave Infrared Domain

Nowadays, fingerprint-based biometric recognition systems are becoming increasingly popular. However, in spite of their numerous advantages, biometric capture devices are usually exposed to the public and thus vulnerable to presentation attacks (PAs). Therefore, presentation attack detection (PAD) methods are of utmost importance in order to distinguish between bona fide and attack presentations. Due to the nearly unlimited possibilities to create new presentation attack instruments (PAIs), unknown attacks are a threat to existing PAD algorithms. This fact motivates research on generalisation capabilities in order to find PAD methods that are resilient to new attacks. In this context, we evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI species. The PAD data is captured in the short wave infrared domain and the results discuss the advantages and drawbacks of this PAD technique regarding unknown attacks.

[1]  Raghavendra Ramachandra,et al.  A Survey on Unknown Presentation Attack Detection for Fingerprint , 2020, INTAP.

[2]  Gian Luca Marcialis,et al.  LivDet 2011 — Fingerprint liveness detection competition 2011 , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Gian Luca Marcialis,et al.  First International Fingerprint Liveness Detection Competition - LivDet 2009 , 2009, ICIAP.

[5]  Jascha Kolberg,et al.  Anomaly Detection With Convolutional Autoencoders for Fingerprint Presentation Attack Detection , 2020, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[6]  T. Fitzpatrick.  The validity and practicality of sun-reactive skin types I through VI. , 1988, Archives of dermatology.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Wael Abd-Almageed,et al.  Fingerprint Presentation Attack Detection Using A Novel Multi-Spectral Capture Device and Patch-Based Convolutional Neural Networks , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).

[9]  Jascha Kolberg,et al.  Fingerprint Presentation Attack Detection using Laser Speckle Contrast Imaging , 2018, 2018 International Conference of the Biometrics Special Interest Group (BIOSIG).

[10]  Arun Ross,et al.  A Survey on Anti-Spoofing Schemes for Fingerprint Recognition Systems , 2014 .

[11]  Gian Luca Marcialis,et al.  LivDet 2013 Fingerprint Liveness Detection Competition 2013 , 2013, 2013 International Conference on Biometrics (ICB).

[12]  Gian Luca Marcialis,et al.  LivDet in Action - Fingerprint Liveness Detection Competition 2019 , 2019, 2019 International Conference on Biometrics (ICB).

[13]  Christoph Busch,et al.  Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks , 2019, IEEE Access.

[14]  Andreas Kolb,et al.  Reliable face anti-spoofing using multispectral SWIR imaging , 2016, 2016 International Conference on Biometrics (ICB).

[15]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[16]  Anil K. Jain,et al.  Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier , 2019, 2019 International Conference on Biometrics (ICB).

[17]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[18]  Gian Luca Marcialis,et al.  LivDet 2017 Fingerprint Liveness Detection Competition 2017 , 2018, 2018 International Conference on Biometrics (ICB).

[19]  Christoph Busch,et al.  Presentation attack detection methods for fingerprint recognition systems: a survey , 2014, IET Biom..

[20]  Stephanie Schuckers,et al.  The effect of environmental conditions and novel spoofing methods on fingerprint anti-spoofing algorithms , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[21]  Wael Abd-Almageed,et al.  On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks , 2019, 2019 International Conference on Biometrics (ICB).

[22]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[23]  Roberto de Alencar Lotufo,et al.  Fingerprint Liveness Detection Using Convolutional Neural Networks , 2016, IEEE Transactions on Information Forensics and Security.

[24]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[25]  Anil K. Jain,et al.  Fingerprint Presentation Attack Detection: Generalization and Efficiency , 2018, 2019 International Conference on Biometrics (ICB).

[26]  F. Roli,et al.  Security evaluation of biometric authentication systems under real spoofing attacks , 2012, IET Biom..

[27]  Jascha Kolberg,et al.  Analysing the Performance of LSTMs and CNNs on 1310 nm Laser Data for Fingerprint Presentation Attack Detection , 2020, 2020 IEEE International Joint Conference on Biometrics (IJCB).

[28]  Anil K. Jain,et al.  Fingerprint Spoof Generalization , 2019, ArXiv.

[29]  Carlo Sansone,et al.  On the Robustness of Fingerprint Liveness Detection Algorithms against New Materials used for Spoofing , 2017, BIOSIGNALS.

[30]  Arun Ross,et al.  An ensemble of one-class SVMs for fingerprint spoof detection across different fabrication materials , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[31]  Ondrej Kanich,et al.  Use of creative materials for fingerprint spoofs , 2018, 2018 International Workshop on Biometrics and Forensics (IWBF).

[32]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[33]  Anil K. Jain.  Technology: Biometric recognition , 2007, Nature.

[34]  Raul Sanchez-Reillo,et al.  A Survey in Presentation Attack and Presentation Attack Detection , 2019, 2019 International Carnahan Conference on Security Technology (ICCST).

[35]  Gian Luca Marcialis,et al.  LivDet 2015 fingerprint liveness detection competition 2015 , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[36]  Anil K. Jain,et al.  Fingerprint Spoof Buster: Use of Minutiae-Centered Patches , 2018, IEEE Transactions on Information Forensics and Security.

[37]  Jascha Kolberg,et al.  Towards Multi-modal Finger Presentation Attack Detection , 2018, 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[38]  Mohamed Hussein,et al.  Multi-Modal Fingerprint Presentation Attack Detection: Evaluation on a New Dataset , 2020, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[39]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[41]  Jascha Kolberg,et al.  Multi-algorithm Benchmark for Fingerprint Presentation Attack Detection with Laser Speckle Contrast Imaging , 2019, 2019 International Conference of the Biometrics Special Interest Group (BIOSIG).

[42]  Nan Li,et al.  Laser Speckle Contrast Imaging: Theory, Instrumentation and Applications , 2013, IEEE Reviews in Biomedical Engineering.

[43]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Arun Ross,et al.  Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials , 2015, IEEE Transactions on Information Forensics and Security.