Auflistung nach Schlagwort "biometrics"
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- KonferenzbeitragAn anomaly detection approach for backdoored neural networks: face recognition as a case study(BIOSIG 2022, 2022) Alexander Unnervik and Sébastien MarcelBackdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how stealthy backdoor attacks are, consequences of these backdoors could be disastrous if such networks were to be deployed for applications as critical as border or access control. In this paper, we propose a novel backdoored network detection method based on the principle of anomaly detection, involving access to the clean part of the training data and the trained network.We highlight its promising potential when considering various triggers, locations and identity pairs, without the need to make any assumptions on the nature of the backdoor and its setup. We test our method on a novel dataset of backdoored networks and report detectability results with perfect scores.
- KonferenzbeitragBiometrics for an ageing society – societal and ethical factors in biometrics and ageing(BIOSIG 2012, 2012) Rebera, Andrew P.; Guihen, BarryBy the middle of the twenty-first century around one third of the European population will be aged 65 or over. This poses two main challenges to biometrics. First, the quality of an image capturable from an older person is likely to be inferior to that of a younger person, leading to increased failure to capture or failure to enroll rates. Second, since biometric features alter over time, `within-person variation' and `template ageing' lead to significant system performance degradation. As society ages the need for solutions becomes increasingly urgent. This paper addresses a major societal and ethical issue this need provokes.
- KonferenzbeitragCan Generative Colourisation Help Face Recognition?(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Drozdowski, Pawel; Fischer, Daniel; Rathgeb, Christian; Geissler, Julian; Knedlik, Jan; Busch, ChristophGenerative colourisation methods can be applied to automatically convert greyscale images to realistically looking colour images. In a face recognition system, such techniques might be employed as a pre-processing step in scenarios where either one or both face images to be compared are only available in greyscale format. In an experimental setup which reflects said scenarios, we investigate if generative colourisation can improve face sample utility and overall biometric performance of face recognition. To this end, subsets of the FERET and FRGCv2 face image databases are converted to greyscale and colourised applying two versions of the DeOldify colourisation algorithm. Face sample quality assessment is done using the FaceQnet quality estimator. Biometric performance measurements are conducted for the widely used ArcFace system with its built-in face detector and reported according to standardised metrics. Obtained results indicate that, for the tested systems, the application of generative colourisation does neither improve face image quality nor recognition performance. However, generative colourisation was found to aid face detection and subsequent feature extraction of the used face recognition system which results in a decrease of the overall false reject rate.
- KonferenzbeitragData Protection Impact Assessment in Identity Control Management with a Focus on Biometrics(Open Identity Summit 2020, 2020) Bisztray, Tamas; Gruschka, Nils; Mavroeidis, Vasileios; Fritsch, LotharPrivacy issues concerning biometric identification are becoming increasingly relevant due to their proliferation in various fields, including identity and access control management (IAM). The General Data Protection Regulation (GDPR) requires the implementation of a data protection impact assessment for privacy critical systems. In this paper, we analyse the usefulness of two different privacy impact assessment frameworks in the context of biometric data protection. We use experiences from the SWAN project that processes four different biometric characteristics for authentication purposes. The results of this comparison elucidate how useful these frameworks are in identifying sector-specific privacy risks related to IAM and biometric identification.
- KonferenzbeitragDiversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage(BIOSIG 2022, 2022) M Charity, Nasir MemonThis work expands on previous advancements in genetic fingerprint spoofing via the DeepMasterPrints and introduces Diversity and Novelty MasterPrints. This system uses quality diversity evolutionary algorithms to generate dictionaries of artificial prints with a focus on increasing coverage of users from the dataset. The Diversity MasterPrints focus on generating solution prints that match with users not covered by previously found prints, and the Novelty MasterPrints explicitly search for prints with more that are farther in user space than previous prints. Our multi-print search methodologies outperform the singular DeepMasterPrints in both coverage and generalization while maintaining quality of the fingerprint image output.
- KonferenzbeitragEEG-based biometrics: phase-locking value from gamma band performs well across heterogeneous datasets(BIOSIG 2022, 2022) Pradeep Kumar G, Utsav DuttaThe performance of functional connectivity metrics is investigated for electroencephalogram (EEG)-based biometrics using a support vector machine classifier. Experiments are conducted on a heterogeneous EEG dataset of 184 subjects formed by pooling three distinct datasets recorded with different systems and protocols. The identification accuracy is found to be higher for higher frequency EEG bands, indicating the enhanced uniqueness of the neural signatures in beta and gamma bands. Using all the 56 EEG channels common to the three databases, the best identification accuracy of 97.4% is obtained using phase locking value-based measures extracted from the gamma frequency band. When the number of channels is reduced to 21 from 56, there is a marginal reduction of 2.4% only in the identification accuracy. Additional experiments are conducted to study the effect of the cognitive state of the subject and mismatched train/test conditions on the system performance.
- KonferenzbeitragFast and Accurate Continuous User Authentication by Fusion of Instance-based, Free-text Keystroke Dynamics(BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Ayotte, Blaine; Banavar, Mahesh K.; Hou, Daqing; Schuckers, StephanieKeystroke dynamics study the way in which users input text via their keyboards, which is unique to each individual, and can form a component of a behavioral biometric system to improve existing account security. Keystroke dynamics systems on free-text data use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Many algorithms require 500, 1,000, or more keystrokes to achieve EERs of below 10%. In this paper, we propose an instancebased graph comparison algorithm to reduce the number of keystrokes required to authenticate users. Commonly used features such as monographs and digraphs are investigated. Feature importance is determined and used to construct a fused classifier. Detection error tradeoff (DET) curves are produced with different numbers of keystrokes. The fused classifier outperforms the state-of-the-art with EERs of 7.9%, 5.7%, 3.4%, and 2.7% for test samples of 50, 100, 200, and 500 keystrokes.
- KonferenzbeitragImpact of Doppelgängers on Face Recognition: Database and Evaluation(BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Rathgeb, Christian; Drozdowski, Pawel; Obel, Marcel; Dörsch, André; Stockhardt, Fabian; Haryanto, Nathania E.; Bernardo, Kevin; Busch, ChristophLook-alikes, a.k.a. doppelgängers, increase the probability of false matches in a facial recognition system, in contrast to random face image pairs selected for non-mated comparison trials. In order to analyse and improve the robustness of automated face recognition, datasets of doppelgänger face image pairs are needed. In this work, we present a new face database consisting of 400 pairs of doppelgänger images. Subsequently, two state-of-the-art face recognition systems are evaluated on said database and other public datasets, including the Disguised Faces in The Wild (DFW) database. It is found that the collected image pairs yield very high similarity scores resulting in a significant increase of false match rates. To facilitate reproducible research and future experiments in this field, the dataset is made available.
- TextdokumentImproving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches(BIOSIG 2017, 2017) Alonso-Fernandez,Fernando; Farrugia,Reuben A.; Bigun,JosefRelaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities.
- KonferenzbeitragIris Recognition in Postmortem Enucleated Eyes(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Saripalle, Sashi K.; McLaughlin, Adam; Derakhshani, RezaThis paper presents a comprehensive multispectral study of iris recognition on postmortem enucleated eyes over a period of three days. An off the shelf iris recognition methodology is employed to analyze the biometric capability of iris in the post mortem setting.We observed that iris patterns of enucleated eyes can provide biometric matches with no false accepts for up to 164 hours after death, albeit with high false rejection rates. We also present our observations on the effects of the environment and other confounding factors that may affect the performance of postmortem iris recognition, with recommendations for rehydration of specimen to regain postmortem biometric utility.