A New Handwritten Signature Verification System Based on the Histogram of Templates Feature and the Joint Use of the Artificial Immune System with SVM

Yasmine Serdouk,Hassiba Nemmour,Youcef Chibani

doi:10.1007/978-3-319-89743-1_11

Yasmine Serdouk, Hassiba Nemmour + Show 1 more

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https://doi.org/10.1007/978-3-319-89743-1_11

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Abstract

Verifying the authenticity of handwritten signatures is required in various current life domains, notably with official contracts, banking or financial transactions. Therefore, in this paper a novel histogram-based descriptor and an improved classification of the bio-inspired Artificial Immune Recognition System (AIRS) are proposed for handwritten signature verification. Precisely, the Histogram Of Templates (HOT) is introduced to characterize the most widespread orientations of local strokes in handwritten signatures, while the combination of AIRS and SVM is proposed to achieve the verification task. Usually, using the k Nearest Neighbor rule, a questioned signature is classified by computing dissimilarities with respect to all AIRS outputs. In this work, using these dissimilarities, a second round of training is achieved by the SVM classifier to further improve the discrimination power. In comparison with existing methods, the experiments on two widely-used datasets show the potential and the effectiveness of the proposed system.

Highlights

Handwritten signature is a biometric feature unique to each person
We deduce that the k Nearest Neighbor (kNN) classification cannot deal with the variability of information offered by the evolved memory cells
In the conventional Artificial Immune Recognition System (AIRS), the verification time corresponds to the calculation of Histogram Of Templates (HOT) features and the computation of the KNN decision for a questioned signature while in SVM, it corresponds to the HOT computation plus the support vector decision time

Summary

Introduction

Handwritten signature is a biometric feature unique to each person. As it depends on physical and psychological conditions of the writer, researchers have to deal with the intra variability of the signer in order to develop robust systems for signature verification. In the off-line approach, signatures are written on a sheet of paper. In this case, features are calculated from the signature shape. The verification can be carried out according to two strategies: writer-dependent or writer-independent [1]. To authenticate genuine and forged signatures, the writer-dependent strategy develops a specific system adapted to each person’s style while only one generic system is developed for all persons in the writer-independent framework

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