%Aigaion2 BibTeX export von HES SO Valais Publications
%Saturday 02 May 2026 01:24:32 PM

@ARTICLE{humm08:spie,
    author = {Humm, Andreas and Hennebert, Jean and Ingold, Rolf},
  keywords = {Biometrics, Signature Verification, Speaker Verification},
     month = {April},
     title = {Spoken Signature For User Authentication},
   journal = {SPIE Journal of Electronic Imaging, Special Section on Biometrics: ASUI January-March 2008},
    volume = {17},
    number = {1},
      year = {2008},
     pages = {011013-1--011013-11},
       url = {http://www.hennebert.org/download/publications/spie-jei-2008_spoken_signature_for_user_authentication.pdf},
       doi = {doi:10.1117/1.2898526},
  abstract = {We are proposing a new user authentication system based on spoken signatures where online signature and speech signals are acquired simultaneously. The main benefit of this multimodal approach is a better accuracy at no extra costs for the user in terms of access time or inconvenience. Another benefit lies in a better robustness against intentional forgeries due to the extra difficulty for the forger to produce both signals. We have set up an experimental framework to measure these benefits on MyIDea, a realistic multimodal biometric database publicly available. More specifically, we evaluate the performance of state-of-the-art modelling systems based on GMM and HMM applied independently to the pen and voice signal where a simple rule-based score fusion procedure is used. We conclude that the best performance is achieved by the HMMs, provided that their topology is optimized on a per user basis. Furthermore, we show that more precise models can be obtained through the use of Maximum a posteriori probability (MAP) training instead of the classically used Expectation Maximization (EM). We also measure the impact of multi-session scenarios versus mono-session scenarios and the impact of skilled versus unskilled signature forgeries attacks.}
}