Nassim Abbas

One-class versus bi-class SVM classifier for off-line signature verification

Support vector machines (SVMs) have become an alternative tool for pattern recognitions, and more specifically for Handwritten Signature Verification Systems (HSVS). Usually, the bi-class SVMs (B-SVM) are used for separating between genuine and forged signatures. However, in practice, only genuine signatures are available. In this paper, we investigate the use of one-class SVM (OC-SVM) for handwritten signature verifications. Experimental results conducted on the standard CEDAR database show the effective use of the one-class SVM compared to the bi-class SVM.

Combination of off-line and on-line signature verification systems based on SVM and DST

The objective of this work is to present a signature verification system based on combination of off-line and online systems for managing conflict provided by the Support Vector Machine (SVM) classifiers. This system is basically divided into three parts: i) off-line verification stage, ii) on-line verification stage and iii) combination module using Dempster-Shafer theory (DST). The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using Dempster-Shafer (DS) rule followed by the likelihood ratio based decision making. Experiments are conducted on the well know NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using DST yields the best verification accuracy compared to the sum rule even when individual off-line and on-line classifications provide conflicting results.

SVM-DSmT combination for off-line signature verification

We propose in this work a signature verification system based on decision combination of off-line signatures for managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Decision making is performed through likelihood ratio. Experiments are conducted on the well known CEDAR database using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework improves the verification accuracy compared to individual SVM classifiers.

Handwritten Digit Recognition Based on a DSmT-SVM Parallel Combination

We propose in this work a new handwritten digit recognition system based on parallel combination of SVM classifiers for managing conflict provided between their outputs. Firstly, we evaluate different methods of generating features to train the SVM classifiers that operate independently of each other. To improve the performance of the system, the outputs of SVM classifiers are combined through the Dezert-Smarandache theory. The proposed framework allows combining the calibrated SVM outputs issued from a sigmoid transformation and uses an estimation technique based on a supervised model to compute the belief assignments. Decision making is performed by maximizing the new Dezert-Smarandache probability. The performance evaluation of the proposed system is conducted on the well known US Postal Service database. Experimental results show that the proposed combination framework improves the recognition rate even when individual SVM classifiers provide conflicting outputs.

SVM-DSmT COMBINATION FOR THE SIMULTANEOUS VERIFICATION OF OFF-LINE AND ON-LINE HANDWRITTEN SIGNATURES

A combination handwritten signature verification system is proposed for managing conflicts provided from each individual off-line and on-line support vector machine (SVM), respectively. Basically, the system is divided into three parts: (i) Off-line verification system, (ii) on-line verification system and (iii) combination module using belief function theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Combination is performed using belief models such as Dempster-Shafer (DS) rule and proportional conflict redistribution (PCR) rule followed by the likelihood ratio-based decision making. Experiments are conducted on the well-known NISDCC signature collection using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework using Dezert-Smarandache (DSm) theory yields the best verification accuracy even when individual off-line and on-line classifications provide conflicting results.