Interval Versus Histogram Of Symbolic Representation Based One-class Classifier For Offline Handwritten Signature Verification

Résumé: This paper proposes a comparison study of using Interval and Histogram of Symbolic Representation (ISR and HSR) based One-Class classifiers, namely OC-ISR and OC-HSR, respectively, applied to the offline signature verification. Usually, symbolic verification models are built straightforward from the feature space. The proposed work explores an alternative approach based on the use of feature-dissimilarities generated from Curvelet Transform (CT) for building the OC-ISR and the OC-HSR classifier. For the OC-ISR classifier, a new weighted membership function is proposed for computing the similarity values between a dissimilarity query vector and a targeted ISR model. The experimental evaluation performed on the well-known public datasets GPDS, CEDAR, and MCYT, reveals the proposed OC-ISR's superiority over the OC-HSR classifier. Moreover, the proposed verification model based on the OC-ISR classifier outperforms the last similar work reported in the literature on the GPDS-160 dataset by 0.99%, 0.8%, and 0.35% of Average Error Rate (AER) for 5, 8, and 12 reference signatures, respectively.

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