Feature combination based on Belief Function Theory for Online Signature Verification

JUAN CARLOS GÓMEZ; MARIANELA PARODI

Valparaíso

Conferencia; 2016 Latin American Computing Conference (CLEI); 2016

Facultad de Ingeniería, Edificio IBC, Pontificia Universidad Católica de Valparaíso, Chile - Casa Central, Universidad Técnica Federico Santa María, Valparaíso, Chile

One of the most challenging problems in online signature verification is to select the best features to model the signatures. A widely used technique to address this problem is to combine different feature sets selected by different criteria. In this paper, the combination of three different feature sets, viz., an automatically selected feature set, a feature set relevant to Forensic Handwriting Experts (FHEs), and a global feature set, on the basis of a score level fusion scheme, is proposed. In order to address the problem of conflicting results appearing when several classifiers are being used, the proposed combination is performed within the framework of the Belief Function Theory (BFT). Two different models, namely, the Denoeux and the Appriou models, are used to embed the problem within this framework, where the fusion is performed resorting to two well-known combination rules, namely, the Dempster-Shafer (DS) and the Proportional Conflict Redistribution (PCR5) one. Experimental results on a publicly available database, prove that the proposed fusion scheme allows the system to have a very good trade-off between verification results and reliability.