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Auflistung nach Schlagwort "Biometric Authentication"

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  • Konferenzbeitrag
    Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent Space
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Chowdhury, Labib; Kamal, Mustafa; Tasnim, Najia; Mohammed, Nabeel
    Deep learning models have become an increasingly preferred option for biometric recognition systems; such as speaker recognition. SincNet, a deep neural network architecture gained popularity in speaker recognition tasks, due to its use of parameterized sinc functions that allow it to work directly on the speech signal. The original SincNet architecture uses the softmax loss which may not be the most suitable choice for recognition-based tasks, as such loss functions do not impose inter-class margins nor does it differentiate between easy and hard training samples. Curriculum learning, particularly those leveraging angular margin-based losses has proven to be very successful in other biometric applications such as face recognition. The advantage of such a curriculum learning-based techniques is that it will impose inter-class margins as well as taking to account easy and hard samples. In this paper, we propose Curricular SincNet (CL-SincNet), an improved SincNet model where we use a curricular loss function to do the training on the SincNet architecture. The proposed model is evaluated on multiple datasets using intra-dataset and inter-dataset evaluation protocol. In both settings, the model performs competitively with other previously published work and in the case of inter-dataset testing, it achieves the best overall results with a reduction of 4% error rate compare to SincNet and other published work.
  • Textdokument
    Evaluation of Motion-based Touch-typing Biometrics in Online Financial Environments
    (BIOSIG 2017, 2017) Buriro,Attaullah; Gupta,Sandeep; Crispo,Bruno
    This paper presents a bimodal scheme, the mechanism which contemplates the way a user enters an 8-digit PIN/password and the phone-movements while doing so, for user authentication in mobile banking/financial applications (apps). The scheme authenticates the user based on the timing differences of the entered strokes. Additionally, it enhances the security by introducing a transparent layer utilizing the phone-movements made by the user. The scheme is assumed to be highly secure as mimicking the invisible touch-timings and the phone-movements could be extremely onerous. Our analysis is based on 2850 samples collected from 95 users through a 3-day unsupervised field experiment and using 3 multi-class classifiers. Random Forest (RF) classifier out-performed other two classifiers and provided a True Acceptance Rate (TAR) of 96%.