Logo des Repositoriums

Auflistung nach Schlagwort "accelerometer"

1 - 2 von 2
  • Konferenzbeitrag
    A Novel Mobilephone Application Authentication Approach based on Accelerometer and Gyroscope Data
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Li, Guoqiang; Bours, Patrick
    The advent of mobile phones have changed our daily life. We are heavily relying on various applications installed on mobilephones to communicate with other, to share personal information with them, and to access our bank account, etc. However, the security measurement in terms of accessing these applications is either omitted or user-hostile because of the burden of memorizing the PIN or password. In order to relieve people from such burden, we explore the possibility of developing a mobilephone application authentication approach by analyzing the accelerometer and gyroscope data collected from the first few seconds when the user opens an application. By evaluating several proposed authentication approaches on a dataset collected from a real-life scenario, we achieve the best EER at 22.72% by only using the data collected from first 3 seconds. We think integrating the proposed non-intrusive authentication approach into the mobilephone application as an alternative for PIN/password can provide a more user-friendly authentication mechanism.
  • Textdokument
    Unobtrusive Gait Recognition using Smartwatches
    (BIOSIG 2017, 2017) Al-Naffakh,Neamah; Clarke,Nathan; Li,Fudong; Haskell-Dowland,Paul
    Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper aims to remedy some of these problems by training and evaluating a smartwatch-based biometric system on data obtained from different days. Also, it proposes an advanced feature selection approach to identify optimal features for each user. Two experiments are presented under three different scenarios: Same-Day, Mixed-Day, and Cross-Day. Competitive results were achieved (best EERs of 0.13% and 3.12% by using the Same day data for accelerometer and gyroscope respectively and 0.69% and 7.97% for the same sensors under the Cross-Day evaluation. The results show that the technology is sufficiently capable and the signals captured sufficiently discriminative to be useful in performing gait recognition.