Open AccessMobile signature verification: feature robustness and performance comparison
Author(s) -
MartinezDiaz Marcos,
Fierrez Julian,
Krish Ram P.,
Galbally Javier
Publication year - 2014
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2013.0081
Subject(s) - computer science , mobile device , discriminative model , robustness (evolution) , signature (topology) , feature selection , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , feature extraction , linear discriminant analysis , benchmark (surveying) , word error rate , data mining , biochemistry , chemistry , linguistics , geometry , mathematics , philosophy , gene , geodesy , geography , operating system
In this study, the effects of using handheld devices on the performance of automatic signature verification systems are studied. The authors compare the discriminative power of global and local signature features between mobile devices and pen tablets, which are the prevalent acquisition device in the research literature. Individual feature discriminant ratios and feature selection techniques are used for comparison. Experiments are conducted on standard signature benchmark databases (BioSecure database) and a state‐of‐the‐art device (Samsung Galaxy Note). Results show a decrease in the feature discriminative power and a higher verification error rate on handheld devices. It is found that one of the main causes of performance degradation on handheld devices is the absence of pen‐up trajectory information (i.e. data acquired when the pen tip is not in contact with the writing surface).