TY - GEN
T1 - A novel personal entropy measure confronted with online signature verification systems' performance
AU - Houmani, Nesma
AU - Garcia-Salicetti, Sonia
AU - Dorizzi, Bernadette
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In this paper, we study the relationship between a novel personal entropy measure for online signatures and the performance of several state-of-the-art classifiers. The entropy measure is based on local density estimation by a Hidden Markov Model. We show that there is a clear relationship between such entropy measure of a person's signature and the behavior of the classifier. We carry out this study on a Dynamic Time Warping classifier, a Gaussian Mixture Model and a Hidden Markov Model as well. It is worth noticing that the HMM classifier differs from the HMM used for entropy computation. Signatures were split into three categories according to their entropy value. These categories are coherent across four different databases of around 100 persons each: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. We studied the impact of such categories on classifier's performance with a larger signature data subset of DS3, of 430 persons.
AB - In this paper, we study the relationship between a novel personal entropy measure for online signatures and the performance of several state-of-the-art classifiers. The entropy measure is based on local density estimation by a Hidden Markov Model. We show that there is a clear relationship between such entropy measure of a person's signature and the behavior of the classifier. We carry out this study on a Dynamic Time Warping classifier, a Gaussian Mixture Model and a Hidden Markov Model as well. It is worth noticing that the HMM classifier differs from the HMM used for entropy computation. Signatures were split into three categories according to their entropy value. These categories are coherent across four different databases of around 100 persons each: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. We studied the impact of such categories on classifier's performance with a larger signature data subset of DS3, of 430 persons.
UR - https://www.scopus.com/pages/publications/67549136241
U2 - 10.1109/BTAS.2008.4699362
DO - 10.1109/BTAS.2008.4699362
M3 - Conference contribution
AN - SCOPUS:67549136241
SN - 9781424427307
T3 - BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems
BT - BTAS 2008 - IEEE 2nd International Conference on Biometrics
T2 - BTAS 2008 - IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems
Y2 - 29 September 2008 through 1 October 2008
ER -