Abstract
To select the best features to model the signatures is one of the major challenges in the field of online signature verification. To combine different feature sets, selected by different criteria, is a useful technique to address this problem. In this line, the analysis of different features and their discriminative power has been researchers’ main concern, paying less attention to the way in which the different kind of features are combined. Moreover, the fact that conflicting results may appear when several classifiers are being used, has rarely been taken into account. In this paper, a score level fusion scheme is proposed to combine three different and meaningful feature sets, viz., an automatically selected feature set, a feature set relevant to Forensic Handwriting Experts (FHEs), and a global feature set. The score level fusion is performed within the framework of the Belief Function Theory (BFT), in order to address the problem of the conflicting results appearing when multiple classifiers are being used. 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. In order to analyze the robustness of the proposed score level fusion approach, the combination is performed for the same verification system using two different classification techniques, namely, Ramdon Forests (RF) and Support Vector Machines (SVM). Experimental results, on a publicly available database, show that the proposed score level fusion approach allows the system to have a very good trade-off between verification results and reliability.
Highlights
Biometric systems aim to automatically recognize or verify an identity
The proposed two-step cascade scheme (Figures 1 and 2) can be performed in different ways depending on which feature sets are selected to train the two independent classifiers involved in the first step combination, and which feature set is left to be combined at the second step
The best option, that is, the one that minimizes the verification errors in these Training Sets, is the one that performs the combination between automatically selected features (ASF) and features relevant to the FHEs (FHEF) features at the first step, and leaves the global features (GF) features to perform the combination at the second step, for both, Ramdon Forests (RF) and Support Vector Machines (SVM) classifiers, and for the Dutch and Chinese datasets
Summary
Biometric systems aim to automatically recognize or verify an identity. Among the numerous available biometric techniques, signature verification is one of the most popular [1,2,3,4,5]. Despite the lack of conclusions, researchers agree that an interesting strategy is to combine different feature sets, selected by different criteria, in order to take advantage of their individual discriminative capabilities In line with this idea, different online signature verification systems have been proposed in the literature combining different kind of features. The combination of three different and meaningful feature sets, selected by different criteria, is proposed on the basis of a score level fusion approach based on the BFT, which would provide the appropriate framework to quantify the confidence in each of the classifiers, and to handle the conflicting results that may appear. The idea is to train an independent classifier using each of the three mentioned feature sets, and combine the corresponding three output scores on the basis of a score level fusion approach based on the BFT.
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