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Auflistung nach Schlagwort "fingerprint"

1 - 6 von 6
  • Konferenzbeitrag
    Case study of the acquisition of contactless fingerprints in a real police setting
    (BIOSIG 2022, 2022) Axel Weissenfeld, Reinhard Schmid
    Biometric 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.
  • Konferenzbeitrag
    Diversity and Novelty MasterPrints: Generating Multiple DeepMasterPrints for Increased User Coverage
    (BIOSIG 2022, 2022) M Charity, Nasir Memon
    This 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.
  • Konferenzbeitrag
    I know who you are: Deanonymization using Facebook Likes
    (Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, 2018) Rüdian, Sylvio; Pinkwart, Niels; Liu, Zhi
    This paper presents a method to deanonymize people using fanpages’ Likes of Facebook users. The strategy shows that information of Likes can be easily crawled from Facebook. Combined with an interactive version of browser-history-stealing it can be used to get identities of users on a website. The attack is possible because of the existence of Facebook’ Likes that can be used as a fingerprint. The claim was tested and discussed with real-world collected data. With the assumption of at least 4 collected Likes per user, 99.91% of them can be deanonymized through the fingerprint of Likes. Apart from that we provide potential solutions for protection of identities in social media.
  • Textdokument
    Recognizing infants and toddlers over an on-production fingerprint database
    (BIOSIG 2017, 2017) Camacho,Vanina; Garella,Guillermo; Franzoni,Francesco; Di Martino,Luis; Carbajal,Guillermo; Preciozzi,Javier; Fernández,Alicia
    It is widely known that biometric systems based on adults fingerprints have reached an outstanding performance when compared against other biometric traits. This explains their extensive use by governmental agencies in charge of citizen identification. Nevertheless, the performance is highly degraded when fingerprints of newborns or toddlers are used. In this work, we analyze the performance of existing solutions (both at sensor and matching level) using 45000 infants fingerprints taken from an on-production civilian database. We also propose a solution by zooming the input fingerprints with an interpolation factor based on ridges distances. The developed solution shows improvements in both fingerprint quality (NFIQ 2.0) as well as recognition performance.
  • Konferenzbeitrag
    Robust Clustering-based Segmentation Methods for Fingerprint Recognition
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Ferreira, Pedro M.; Sequeira, Ana F.; Cardoso, Jaime S.; Rebelo, Ana
    Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option - a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.
  • 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.