Online Signature Verification: To What Extent Should a Classifier be Trusted in?
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 the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Centro Latinoamericano de Estudios en Informática
2017-08-01
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| Series: | CLEI Electronic Journal |
| Online Access: | http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/24 |
| _version_ | 1811278348457869312 |
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| author | Marianela Parodi Juan C. Gómez |
| author_facet | Marianela Parodi Juan C. Gómez |
| author_sort | Marianela Parodi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:33:06Z |
| format | Article |
| id | doaj.art-2fa0be6da0914b0fb3ffebcade08e1ad |
| institution | Directory Open Access Journal |
| issn | 0717-5000 |
| language | English |
| last_indexed | 2024-04-13T00:33:06Z |
| publishDate | 2017-08-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj.art-2fa0be6da0914b0fb3ffebcade08e1ad2022-12-22T03:10:24ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002017-08-012025:15:1610.19153/cleiej.20.2.524Online Signature Verification: To What Extent Should a Classifier be Trusted in?Marianela ParodiJuan C. GómezTo 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.http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/24 |
| spellingShingle | Marianela Parodi Juan C. Gómez Online Signature Verification: To What Extent Should a Classifier be Trusted in? CLEI Electronic Journal |
| title | Online Signature Verification: To What Extent Should a Classifier be Trusted in? |
| title_full | Online Signature Verification: To What Extent Should a Classifier be Trusted in? |
| title_fullStr | Online Signature Verification: To What Extent Should a Classifier be Trusted in? |
| title_full_unstemmed | Online Signature Verification: To What Extent Should a Classifier be Trusted in? |
| title_short | Online Signature Verification: To What Extent Should a Classifier be Trusted in? |
| title_sort | online signature verification to what extent should a classifier be trusted in |
| url | http://www.clei.org/cleiej-beta/index.php/cleiej/article/view/24 |
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