P329 - BIOSIG 2022 - Proceedings of the 21st International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragAnalysis of Minutiae Quality for Improved Workload Reduction in Fingerprint Identification(BIOSIG 2022, 2022) Daile Osorio-Roig, Tim RohwedderThe workload of biometric identification in large fingerprint databases poses a challenging problem. Efficient schemes for biometric workload reduction are a topic of ongoing research. Some of the state-of-the art approaches rely on triangles of minutia points generated by Delaunay triangulation, which are then used for indexing. In this paper, we investigate how quality estimation at the minutia level can improve the performance of such algorithms and hence the system workload. In order to reduce the number of spurious and missing minutiae, we analyse the impact of selecting minutiae points based on their qualities. This, in turn, can significantly distort the triangulation. In addition, we consider the usefulness of the average minutia quality as an additional criteria of the minutia triangles for indexing. Our results show that both strategies lead to a significant reduction in biometric workload compared to a baseline solution (i.e. exhaustive search) – down to 36% on average.
- 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.
- KonferenzbeitragBIOSIG 2022 - Komplettband(BIOSIG 2022, 2022)
- KonferenzbeitragCan point-cloud based neural networks learn fingerprint variability?(BIOSIG 2022, 2022) Dominik Söllinger, Robert JöchlSubject- and environmental-specific variations affect the fingerprint recognition process. Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. Experiments show that point-cloud based neural networks are capable of distinguishing between the different sessions in the PLUS-MSL-FP dataset solely based on FP minutiae locations.
- KonferenzbeitragCase study of the acquisition of contactless fingerprints in a real police setting(BIOSIG 2022, 2022) Axel Weissenfeld, Reinhard SchmidBiometric recognition systems integrated into mobile devices have gained acceptance during recent years. Developments in fingerprint acquisition technology have resulted in touchless mobile devices that acquire high quality fingerprints. While authorities are particular interested on mobile solutions, they have databases containing fingerprint data mainly acquired using contactbased devices. Therefore, they are interested in the accuracy of cross-sensor fingerprint recognition. We present a case study of a comprehensive matching comparison on real fingerprint data acquired by national police officers. The objective of this study is: (i) to analyse the feasibility when comparing data acquired using a typical contact-based fingerprint device against data acquired using a new contactless device, and (ii) the feedback of the end user (i.e. national police officers) regarding the acquisition process. Obtained results are promising and the current prototype shows its feasibility for operational police use. The end users expressed their satisfaction with the developed prototype and they suggested extra functionalities towards a practical solution for police officers.
- KonferenzbeitragCross-Sensor Finger Vein Recogition(BIOSIG 2022, 2022) Bernhard Prommegger, Georg Wimmer and Andreas UhlIf biometric systems are rolled out on a large scale, it will not always be guaranteed that all capturing devices are of exactly the same type. It is therefore important to ensure that the biometric system also works across multiple capturing devices. In finger vein biometry, there is almost no published work in this regard. The biggest problem here is certainly that there are only very few datasets (recorded by different institutions) that usually have no overlap at all with the test persons contained. In a first approach, this article tries to examine how well cross-sensor finger vein recognition works. For the investigation, four publicly available datasets, which were acquired with four different devices in three different scenarios, were evaluated. Using three different finger vein recognition approaches, we will show that the results distinctly deteriorate in cross-sensor recognition scenarios compared to the recognition results using only images from the same device, even more so for image data from contact-less and contact-based capturing devices
- KonferenzbeitragDeDiM: De-identification using a diffusion model(BIOSIG 2022, 2022) Hidetsugu Uchida, Narishige Abe and Shigefumi YamadaAs a countermeasure against malicious authentication in a face recognition system using a face image obtained from SNS or the like, de-identification methods based on adversarial example have been studied. However, since adversarial example directly uses the gradient information of a face recognition model, it is highly dependent on the model, and a de-identification effect and image quality are difficult to achieve for an unknown recognition model. In this study, we propose a novel de-identification method based on a diffusion model, which has high generalizability to an unknown recognition model by applying minute changes to face shapes. Experiments using LFW showed that the proposed method has a higher de-identification effect for unknown models and better image quality than a conventional method using adversarial example.
- KonferenzbeitragDeep Coupled GAN-Based Score-Level Fusion for Multi-Finger Contact to Contactless Fingerprint Matching(BIOSIG 2022, 2022) Md Mahedi Hasan, Nasser Nasrabadi and Jeremy DawsonInteroperability between contact to contactless images in fingerprint matching is a key factor in the success of contactless fingerprinting devices, which have recently witnessed an increasing demand for biometric authentication. However, due to the presence of perspective distortion and the absence of elastic deformation in contactless fingerphotos, direct matching between contactless fingerprint probe images and legacy contact-based gallery images produces a low accuracy. In this paper, to improve interoperability, we propose a coupled deep learning framework that consists of two Conditional Generative Adversarial Networks. Generative modeling is employed to find a projection that maximizes the pairwise correlation between these two domains in a common latent embedding subspace. Extensive experiments on three challenging datasets demonstrate significant performance improvements over the state-of-the-art methods and two top-performing commercial off-the-shelf SDKs, i.e., Verifinger 12.0 and Innovatrics. We also achieve a high-performance gain by combining multiple fingers of the same subject using a score fusion model.
- 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.