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Auflistung nach Schlagwort "Continuous authentication"

1 - 4 von 4
  • Konferenzbeitrag
    DEFT: A new distance-based feature set for keystroke dynamics
    (BIOSIG 2023, 2023) Kaluarachchi, Nuwan; Kandanaarachchi, Sevvandi; Moore, Kristen; Arakala, Arathi
    Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person’s typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.
  • Konferenzbeitrag
    Gait Authentication based on Spiking Neural Networks
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Rúa, Enrique Argones; van Hamme, Tim; Preuveneers, Davy; Joosen, Wouter
    In this paper we address gait authentication using a novel approach based on spiking neural networks (SNNs). This technology has proven advantages regarding energy consumption and it is a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using this technology is the training of the network itself, since it is not straightforward to apply well-known error backpropagation, massively used in traditional artificial neural networks (ANNs). In this paper we propose a new derivation of error backpropagation for the spiking neural networks that integrates lateral inhibition and provides competitive results when compared to state of the art ANNs in the context of IMU-based gait authentication.
  • Konferenzbeitrag
    Impact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Two Behavioral Biometric Datasets
    (BIOSIG 2023, 2023) Ahmed Wahab, Daqing Hou
    Deep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impact of dataset breadth (i.e., the number of subjects) and depth (i.e., the amount of data per subject) on the performance of these models remain unexplored. To this end, we have conducted extensive experiments using two publicly available large datasets (Aalto and BrainRun), varying both the number of training subjects and the number of samples per subject. Our results show that dataset depth plays a crucial role in capturing more intricate variations specific to individual subjects, thereby positively influencing the performance of the SNN models. On the other hand, increasing the dataset breadth enables the model to effectively capture more inter-subject variability, which proved to be a more significant factor in improving the overall model performance. Specifically, once a certain threshold for the number of training subjects is surpassed, breadth starts to dominate performance and the impact of dataset depth diminishes and disappears. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems.
  • Konferenzbeitrag
    A Wrist-worn Diffuse Optical Tomography Biometric System
    (BIOSIG 2023, 2023) Satya Sai Siva Rama Krishna Akula, Sumanth Dasari
    We present a diffuse optical tomography-based biometric system that is not dependent on external traits such as face, but rather interior anatomical information for better privacy and security. The Diffuse Optical Tomography (DOT) scanner is in the form of a wearable over the lower forearm and the wrist, where anatomical structures in the optical path of the scanner optodes serve as the basis for the unique biometric patterns. Our DOT scanner is low-cost and uses COTS Near Infrared LEDs and sensors. To supplement the DOT, our design also incorporates wrist vein imaging as a secondary modality. This paper details the design of the wristband, data collection, and machine learning-based analysis to show the utility of the DOT as a stand-alone biometric modality, and the efficacy of fusing DOT and wrist vein modalities. Our early experimental findings show promise, achieving a high area under the receiver operating characteristic curve (0.989).