Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

Online Signature Verification: Improving Performance through Pre-classification Based on Global Features

In this paper, a pre-classification stage based on global features is incorporated to an online signature verification system for the purposes of improving its performance. The pre-classifier makes use of the discriminative power of some global features to discard (by declaring them as forgeries) those signatures for which the associated global feature is far away from its respective mean. For the remaining signatures, features based on a wavelet approximation of the time functions associated with the signing process, are extracted, and a Random Forest based classification is performed. The experimental results show that the proposed pre-classification approach, when based on the apppropriate global feature, is capable of getting error rate improvements with respect to the case where no pre-classification is performed. The approach also has the advantages of simplifying and speeding up the verification process.Fil: Parodi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Gómez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentin

Incorporating image quality in multi-algorithm fingerprint verification

The final publication is available at Springer via http://dx.doi.org/10.1007/11608288_29Proceedings of International Conference, ICB 2006, Hong Kong (China)The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.This work has been supported by Spanish MCYT TIC2003-08382-C05-01 and by European Commission IST-2002-507634 Biosecure NoE projects

Cryptographic Key Generation Using Handwritten Signature

Based on recent works showing the feasibility of key generation using biometrics, we study the application of handwritten signature to cryptography. Our signature-based key generation scheme implements the cryptographic construction named fuzzy vault. The use of distinctive signature features suited for the fuzzy vault is discussed and evaluated. Experimental results are reported, including error rates to unlock the secret data by using both random and skilled forgeries from the MCYT database

Fusion strategies in multimodal biometric verification

The aim of this paper, regarding multimodal biometric verification, is twofold: on the one hand, to review some score fusion strategies reported in the literature and, on the other hand, to compare experimentally a selection of them using as monomodal baseline systems our template-based face, minutiaebased fingerprint and HMM-based on-line signature verification systems on the MCYT multimodal database. A new strategy is proposed and discussed in order to compute a multimodal combined score by means of Support Vector Machine (SVM) classifiers. 1

Sensor interoperability and fusion in signature verification : a case study using Tablet PC

Several works related to information fusion for signature verification have been presented. However, few works have focused on sensor fusion and sensor interoperability. In this paper, these two topics are evaluated for signature verification using two different commercial Tablet PCs. An enrolment strategy using signatures from the two Tablet PCs is also proposed. Authentication performance experiments are reported by using a database with over 3000 signatures. © Springer-Verlag Berlin Heidelberg 2005

Forensic Identification Reporting Using Automatic Speaker Recognition Systems

In this paper, we will show how any speaker recognition system can be adapted to provide its results according to the bayesian approach for evidence analysis and forensic reporting. This approach, firmly established in other forensic areas as fingerprint, DNA or fiber analysis, suits the needs of both the court and the forensic scientist. We will show the inadequacy of the classical approach to forensic reporting because of the use of thresholds and the suppression of the prior probabilities related to the case. We will also show how to assess the performance of those forensic systems through Tippet plots. Finally, an example is shown using NIST-Ahumada eval'2001 data, where the speaker recognition abilities of our system are assessed through DET plots, using then these raw scores as evidences into the forensic system, where relative to populations we will obtain the corresponding likelihood ratios values, which are assessed through Tippet plots