Dynamic ensemble selection for off-line signature verification

Authors:
Luana Batista;Eric Granger;Robert Sabourin
Affiliations:
Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Montréal, QC, Canada;Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Montréal, QC, Canada;Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Montréal, QC, Canada
Venue:
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Year:
2011

Citing 6
Cited 2

Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries

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An Off-Line Signature Verification Method Based on the Questioned Document Expert's Approach and a Neural Network Classifier

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From dynamic classifier selection to dynamic ensemble selection

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Improving performance of HMM-based off-line signature verification systems through a multi-hypothesis approach

International Journal on Document Analysis and Recognition
A multi-classifier system for off-line signature verification based on dissimilarity representation

MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems

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From static to dynamic ensemble of classifiers selection: Application to Arabic handwritten recognition

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Abstract

Although not in widespread use in Signature Verification (SV), the performance of SV systems may be improved by using ensemble of classifiers (EoC). Given a diversified pool of classifiers, the selection of a subset to form an EoC may be performed either statically or dynamically. In this paper, two new dynamic selection (DS) strategies are proposed, namely OP-UNION and OP-ELIMINATE, both based on the K-nearest-oracles. To compare ensemble selection strategies, a hybrid generative-discriminative system for off-line SV system is considered. Experiments performed by using real-world SV data, comprised of genuine samples, and random, simple and skilled forgeries, indicate that the proposed DS strategies achieve a significantly higher level of performance in off-line SV than other well-known DS and static selection (SS) strategies. Improvements are most notable in problems where a significant level of uncertainty emerges due a considerable amount of intra-class variability.