Nesma Houmani

Fingerprint and On-Line Signature Verification Competitions at ICB 2009

This paper describes the objectives, the tasks proposed to the participants and the associated protocols in terms of database and assessment tools of two competitions on fingerprints and on-line signatures. The particularity of the fingerprint competition is to be an on-line competition, for evaluation of fingerprint verification tools such as minutiae extractors and matchers as well as complete systems. This competition will be officialy launched during the ICB conference. The on-line signature competition will test the influence of multi-sessions, environmental conditions (still and mobility) and signature complexity on the performance of complete systems using two datasets extracted from the BioSecure database. Its result will be presented during the ICB conference.

On assessing the robustness of pen coordinates, pen pressure and pen inclination to time variability with personal entropy

In this work, we study different combinations of the five time functions captured by a digitizer in presence or not of time variability. To this end, we propose two criteria independent of the classification step: Personal Entropy, introduced in our previous works and an intra-class variability measure based on Dynamic Time Warping. We confront both criteria to system performance using a Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). Moreover, we introduce the concept of short-term time variability, proposed on MCYT-100, and long-term time variability studied with BIOMET database. Our experiments clarify conflicting results in the literature and confirm some other: pen inclination angles are very unstable in presence or not of time variability; the only combination which is robust to time variability is that containing only coordinates; finally, pen pressure is not recommended in the long-term context, although it may give better results in terms of performance (according to the classifier used) in the short-term context.

A novel criterion for writer enrolment based on a time-normalized signature sample entropy measure

This paper proposes a novel criterion for an improved writer enrolment based on an entropy measure for online genuine signatures. As online signature is a temporal signal, we measure the time-normalized entropy of each genuine signature, namely, its average entropy per second. Entropy is computed locally, on portions of a genuine signature, based on local density estimation by a Client-Hidden Markov Model. The average time-normalized entropy computed on a set of genuine signatures allows then categorizing writers in an unsupervised way, using a K-Means algorithm. Linearly separable and visually coherent classes of writers are obtained on MCYT-100 database and on a subset of BioSecure DS2 containing 104 persons (DS2-104). These categories can be analyzed in terms of variability and complexity measures that we have defined in this work. Moreover, as each category can be associated with a signature prototype inherited from the K-Means procedure, we can generalize the writer categorization process on the large subset DS2-382 from the same DS2 database, containing 382 persons. Performance assessment shows that one category of signatures is significantly more reliable in the recognition phase, and given the fact that our categorization can be used online, we propose a novel criterion for enhanced writer enrolment.

A Novel Personal Entropy Measure confronted with Online Signature Verification Systems' Performance

In this paper, we study the relationship between a novel personal entropy measure for online signatures and the performance of several state-of-the-art classifiers. The entropy measure is based on local density estimation by a hidden Markov model. We show that there is a clear relationship between such entropy measure of a persons signature and the behavior of the classifier. We carry out this study on a dynamic time warping classifier, a Gaussian mixture model and a hidden Markov model as well. It is worth noticing that the HMM classifier differs from the HMM used for entropy computation. Signatures were split into three categories according to their entropy value. These categories are coherent across four different databases of around 100 persons each: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. We studied the impact of such categories on classifiers performance with a larger signature data subset of DS3, of 430 persons.

A client-entropy measure for On-line Signatures

In this article, we propose an original way to characterize information content in online signatures through a client-entropy measure based on local density estimation by a hidden Markov model. We show that this measure can be used to categorize signatures in visually coherent classes that can be related to complexity and variability criteria. Besides, the generated categories are coherent across four different databases: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. This measure allows a comparison of databases in terms of clientspsila signatures according to their information content.

On measuring forgery quality in online signatures

This work proposes a novel measure to quantify the quality of a skilled forgery sample in the online signature framework. Such a quality measure is constructed by adapting our former Personal Entropy to the context of skilled forgeries production. For validation, we confront our quality measure to several types of skilled forgeries (static, dynamic, professional) captured on different acquisition platforms. Indeed, four databases are exploited: MCYT-100, Philips database, BioSecure data subsets DS2 and DS3. We prove the effectiveness of our quality measure to quantify the quality of all types of skilled forgeries available with regards to the performance of three classifiers: a Dynamic Time Warping, a Hidden Markov models and a Gaussian Mixture Model.

Online Handwritten Signature Verification

In this chapter, we first provide an overview of the existing main approaches, databases, evaluation campaigns and the remaining challenges in online handwritten signature verification. We then propose a new benchmarking framework for online signature verification by introducing the concepts of “Reference Systems”, “Reference Databases” and associated “Reference Protocols.” Finally, we present the results of several approaches within the proposed evaluation framework. Among them are also present the best approaches within the first international Signature Verification Competition held in 2004 (SVC’2004), Dynamic Time Warping and Hidden Markov Models. All these systems are evaluated first within the benchmarking framework and also with other relevant protocols. Experiments are also reported on two different databases (BIOMET and MCYT) showing the impact of time variability for online signature verification. The two reference systems presented in this chapter are also used and evaluated in the BMEC’2007 evaluation campaign, presented in Chap11.

On-line Signature Verification on a Mobile Platform

This paper concerns the implementation of our online signature verification system on a mobile device. Verification involves confirming or denying a person‘s claimed identity. Our system is based on a Hidden Markov Model and outputs two complementary scores: the first one is related to the likelihood given by the HMM of the claimed identity; the second one is related to the segmentation given by such an HMM on the input signature. A claimed identity is confirmed when the arithmetic mean of the two scores obtained on such an input signature is higher than a threshold. Also, a personal normalization of the local parameters of the signature is carried out to make the system robust to changes of platforms. A patent was submitted with special emphasis on the latter claim. This system is implemented on a mobile platform PDA Qtek 2020 ARM 400 MHz. An acquisition interface is developed allowing an enrollment step of a person by acquisition of 5 of his/her signatures, and a verification step of a given signature of a registered person. Enrolment speed depends on the complexity of the signature, while verification is performed in real time. Performance assessment of our system, carried out on two databases acquired on a PDA, shows a degradation of system performance on mobile platform compared to a fixed platform. In order to improve the performance in the case of mobility, we propose a strategy for enhancing the quality of the reference signatures at the enrolment phase.

A Signature Complexity Measure to Select Reference Signatures for Online Signature Verification

This paper presents an original procedure for selecting the reference online signature instances of a writer, an important issue for any effective signature verifier. To this end, for each signature instance, we propose a novel complexity measure, by exploiting a global description of signatures in the frequency domain as well as a global statistical modelling of each signature instance. To select the reference signatures, we propose a method based on the distribution of complexity values for all the available genuine signatures. The 2500 genuine samples of MCYT-100 online database are used in this study. Experimental results show the effectiveness of the method and of the here proposed complexity measure for this specific task.