A comparison of SVM and HMM classifiers in the off-line signature verification

Authors:
Edson J. R. Justino;Flávio Bortolozzi;Robert Sabourin
Affiliations:
PUCPR-Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceiçáo, 1155, Curitiba, CEP 80215-901, PR, Brazil;PUCPR-Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceiçáo, 1155, Curitiba, CEP 80215-901, PR, Brazil;íTS-ícole de Technologie Supérieure, 1100, rue Notre-Dame Ouest, Montréal, Québec, Canada H3C 1K3
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off-line signature verification. For this purpose, an appropriate learning and testing protocol was created to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity using random, simple and simulated forgeries.