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Auflistung nach Autor:in "Kuijper, Arjan"

1 - 4 von 4
  • Konferenzbeitrag
    Compact Models for Periocular Verification Through Knowledge Distillation
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Boutros, Fadi; Damer, Naser; Fang, Meiling; Raja, Kiran; Kirchbuchner, Florian; Kuijper, Arjan
    Despite the wide use of deep neural network for periocular verification, achieving smaller deep learning models with high performance that can be deployed on low computational powered devices remains a challenge. In term of computation cost, we present in this paper a lightweight deep learning model with only 1.1m of trainable parameters, DenseNet-20, based on DenseNet architecture. Further, we present an approach to enhance the verification performance of DenseNet-20 via knowledge distillation. With the experiments on VISPI dataset captured with two different smartphones, iPhone and Nokia, we show that introducing knowledge distillation to DenseNet-20 training phase outperforms the same model trained without knowledge distillation where the Equal Error Rate (EER) reduces from 8.36% to 4.56% EER on iPhone data, from 5.33% to 4.64% EER on Nokia data, and from 20.98% to 15.54% EER on cross-smartphone data.
  • Konferenzbeitrag
    The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Damer, Naser; Grebe, Jonas Henry; Chen, Cong; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, Arjan
    Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases.We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.
  • Konferenzbeitrag
    Face Presentation Attack Detection in Ultraviolet Spectrum via Local and Global Features
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Siegmund, Dirk; Kerckhoff, Florian; Magdaleno, Javier Yeste; Jansen, V; Kirchbuchner, Florian; Kuijper, Arjan
    The security of the commonly used face recognition algorithms is often doubted, as they appear vulnerable to so-called presentation attacks. While there are a number of detection methods that are using different light spectra to detect these attacks this is the first work to explore skin properties using the ultraviolet spectrum. Our multi-sensor approach consists of learning features that appear in the comparison of two images, one in the visible and one in the ultraviolet spectrum. We use brightness and keypoints as features for training, experimenting with different learning strategies. We present the results of our evaluation on our novel Face UV PAD database. The results of our method are evaluated in an leave-one-out comparison, where we achieved an APCER/BPCER of 0%/0.2%. The results obtained indicate that UV images in presentation attack detection include useful information that are not easy to overcome.
  • Konferenzbeitrag
    Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Terhörst, Philipp; Huber, Marco; Damer, Naser; Rot, Peter; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan
    Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.