Auflistung nach Schlagwort "Presentation attack detection"
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- KonferenzbeitragAdvanced Face Presentation Attack Detection on Light Field Database(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Chiesa, Valeria; Dugelay, Jean-LucIn the last years several works have been focused on the impact of new sensors on face recognition. A particular interest has been addressed to technologies able to detect the depth of the scene as light field cameras. Together with person identification algorithms, new anti-spoofing methods customized for specific devices have to be investigated. In this paper, a new algorithm for presentation attack detection on light field face database is proposed. While distance between subject and camera is not a relevant information for standard 2D spoofing attacks, it could be important when using 3D cameras. We prove through three experiments that the proposed method based on depth map elaboration outperforms the existent algorithms in presentation attack detection on light field images.
- KonferenzbeitragFisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detection(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) González-Soler, Lázaro J.; Gomez-Barrero, Marta; Busch, ChristophThe task of determining whether a sample stems from a real subject (i.e, it is a bona fide presentation) or it comes from an artificial replica (i.e., it is an attack presentation) is a mandatory requirement for biometric capture devices, which has received a lot of attention in the recent past. Nowadays, most face Presentation Attack Detection (PAD) approaches have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAIs) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the evaluation. For those more realistic scenarios, the existing approaches are in many cases unable to detect unknown PAI species. In this work, we introduce a new feature space based on Fisher vectors, computed from compact Binarised Statistical Image Features (BSIF) histograms, which allows finding semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated over three freely available facial databases, shows promising results in the top state-of-the-art: a BPCER100 under 17% together with a AUC over 98% can be achieved in the presence of unknown attacks.
- TextdokumentOn the Generalization of Fused Systems in Voice Presentation Attack Detection(BIOSIG 2017, 2017) Gonçalves,André R.; Korshunov,Pavel; Violato,Ricardo P.V.; Simões,Flávio O.; Marcel,SébastienThis paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feedforward neural network classifiers. The systems achieved the top five performances in the competition. We present the proposed systems and analyze the calibration and fusion strategies employed. To assess the systems’ generalization capacity, we evaluated it on an unrelated larger database recorded in Portuguese language, which is different from the English language used in the competition. These extended evaluation results show that the fusion-based system, although successful in the scope of the evaluation, lacks the ability to accurately discriminate genuine data from attacks in unknown conditions, which raises the question on how to assess the generalization ability of attack detection systems in practical application scenarios.
- KonferenzbeitragShuffled Patch-Wise Supervision for Presentation Attack Detection(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Kantarcı, Alperen; Dertli, Hasan; Ekenel, Hazım KemalFace anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets ---Replay-Mobile, OULU-NPU--- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.
- KonferenzbeitragTowards Contactless Fingerprint Presentation Attack Detection using Algorithms from the Contact-based Domain(BIOSIG 2023, 2023) Jannis Priesnitz, Roberto CasulaIn this work, we investigate whether contact-based fingerprint Presentation Attack Detection (PAD) methods can generalize to the contactless domain. To this end, we selected a state-of-the-art patch-based fingerprint PAD algorithm which achieved high detection performance in the contact-based domain and adapted it for contactless fingerprints. We train and test the method using three contactless fingerprint databases and evaluate its generalization capabilities using Leave-One-Out (LOO) protocols. Further, we acquired a new PAD database and use it in a cross-database evaluation. The adopted method shows low error rates in most scenarios and can generalize to unseen contactless presentation attacks.