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Auflistung nach Schlagwort "presentation attack detection"

1 - 4 von 4
  • Textdokument
    Impact of bandwidth and channel variation on presentation attack detection for speaker verification
    (BIOSIG 2017, 2017) Delgado,Héctor; Todisco,Massimiliano; Evans,Nicholas; Sahidullah,Md; Liu,Wei Ming; Alegre,Federico; Kinnunen,Tomi; Fauve,Benoit
    Vulnerabilities to presentation attacks can undermine confidence in automatic speaker verification (ASV) technology. While efforts to develop countermeasures, known as presentation attack detection (PAD) systems, are now under way, the majority of past work has been performed with high-quality speech data. Many practical ASV applications are narrowband and encompass various coding and other channel effects. PAD performance is largely untested in such scenarios. This paper reports an assessment of the impact of bandwidth and channel variation on PAD performance. Assessments using two current PAD solutions and two standard databases show that they provoke significant degradations in performance. Encouragingly, relative performance improvements of 98% can nonetheless be achieved through feature optimisation. This performance gain is achieved by optimising the spectro-temporal decomposition in the feature extraction process to compensate for narrowband speech. However, compensating for channel variation is considerably more challenging.
  • Konferenzbeitrag
    Improved Liveness Detection in Dorsal Hand Vein Videos using Photoplethysmography
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Schuiki, Johannes; Uhl, Andreas
    In this study, a previously published infrared finger vein liveness detection scheme is tested for its applicability on dorsal hand vein videos. A custom database consisting of five different types of presentation attacks recorded with transillumination as well as reflected light illumination is examined. Additionally, two different methods for liveness detection are presented in this work. All methods described employ the concept of generating a signal through the change in average pixel illumination, which is referred to as Photoplethysmography. Feature vectors in order to classify a given video sequence are generated using spectral analysis of the time series. Experimental results show the effectiveness of the proposed methods.
  • Konferenzbeitrag
    Learning by Environment Cluster s for Face Presentation Attack Detection
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Matsunami, Tomoaki; Uchida, Hidetsugu; Abe, Narishige; Yamada, Shigefumi
    Face recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification method
  • Konferenzbeitrag
    Towards Fingerprint Presentation Attack Detection Based on Convolutional Neural Networks and Short Wave Infrared Imaging
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Tolosana, Ruben; Gomez-Barrero, Marta; Kolberg, Jascha; Morales, Aythami; Busch, Christoph; Ortega-Garcia, Javier
    Biometric recognition offers many advantages over traditional authentication methods, but they are also vulnerable to, for instance, presentation attacks. These refer to the presentation of artifacts, such as facial pictures or gummy fingers, to the biometric capture device, with the aim of impersonating another person or to avoid being recognised. As such, they challenge the security of biometric systems and must be prevented. In this paper, we present a new fingerprint presentation attack detection method based on convolutional neural networks and multi-spectral images extracted from the finger in the short wave infrared spectrum. The experimental evaluation, carried out on an initial small database but comprising different materials for the fabrication of the artifacts and including unknown attacks for testing, shows promising results: all samples were correctly classified.