TY - GEN
T1 - A client-entropy measure for on-line signatures
AU - Salicetti, Sonia Garcia
AU - Houmani, Nesma
AU - Dorizzi, Bernadette
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In this article, we propose an original way to characterize information content in Online Signatures through a client-entropy measure based on local density estimation by a Hidden Markov Model. We show that this measure can be used to categorize signatures in visually coherent classes that can be related to complexity and variability criteria. Besides, the generated categories are coherent across four different databases: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. This measure allows a comparison of databases in terms of clients' signatures according to their information content.
AB - In this article, we propose an original way to characterize information content in Online Signatures through a client-entropy measure based on local density estimation by a Hidden Markov Model. We show that this measure can be used to categorize signatures in visually coherent classes that can be related to complexity and variability criteria. Besides, the generated categories are coherent across four different databases: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. This measure allows a comparison of databases in terms of clients' signatures according to their information content.
KW - Complexity
KW - Entropy
KW - On-line signature
KW - Signature categorization
KW - Variability
UR - https://www.scopus.com/pages/publications/56749096454
U2 - 10.1109/BSYM.2008.4655527
DO - 10.1109/BSYM.2008.4655527
M3 - Conference contribution
AN - SCOPUS:56749096454
SN - 9781424425679
T3 - 2008 Biometrics Symposium, BSYM
SP - 83
EP - 88
BT - 2008 Biometrics Symposium, BSYM
T2 - 2008 Biometrics Symposium, BSYM
Y2 - 23 September 2008 through 25 September 2008
ER -