Julian Fierrez-Aguilar

An on-line signature verification system based on fusion of local and global information

An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Global information is extracted with a feature-based representation and recognized by using Parzen Windows Classifiers. Local information is extracted as time functions of various dynamic properties and recognized by using Hidden Markov Models. Experimental results are given on the large MCYT signature database (330 signers, 16500 signatures) for random and skilled forgeries. Feature selection experiments based on feature ranking are carried out. It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available. Conversely, it is found that global analysis is more appropriate in the case of small training set size. The two proposed systems are also shown to give complementary recognition information which is successfully exploited using decision-level score fusion.

Target dependent score normalization techniques and their application to signature verification

Score normalization methods in biometric verification, which encompass the more traditional user-dependent decision thresholding techniques, are reviewed from a test hypotheses point of view. These are classified into test dependent and target dependent methods. The focus of the paper is on target dependent score normalization techniques, which are further classified into impostor-centric, target-centric, and target-impostor methods. These are applied to an on-line signature verification system on signature data from the First International Signature Verification Competition (SVC 2004). In particular, a target-centric technique based on the cross-validation procedure provides the best relative performance improvement testing both with skilled (19%) and random forgeries (53%) as compared to the raw verification performance without score normalization (7.14% and 1.06% Equal Error Rate for skilled and random forgeries, respectively).

Rapid and brief communication: Discriminative multimodal biometric authentication based on quality measures

A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.

Incorporating image quality in multi-algorithm fingerprint verification

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.

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.

A comparative evaluation of fusion strategies for multimodal biometric verification

The aim of this paper, regarding multimodal biometric verification, is twofold: on the one hand, some score fusion strategies reported in the literature are reviewed and, on the other hand, we compare experimentally a selection of them using as monomodal baseline experts: i) our face verification system based on a global face appearance representation scheme, ii) our minutiae-based fingerprint verification system, and iii) our on-line signature verification system based on HMM modeling of temporal functions, on the MCYT multimodal database. A new strategy is also proposed and discussed in order to generate a multimodal combined score by means of Support Vector Machine (SVM) classifiers from which user-independent and user-dependent fusion schemes are derived and evaluated.

Multimodal biometric authentication using quality signals in mobile communications

The elements of multimodal authentication along with system models are presented. These include the machine experts as well as machine supervisors. In particular, fingerprint and speech based systems serve as illustration of a mobile authentication application. A novel signal adaptive supervisor, based on the input biometric signal quality, is evaluated. Experimental results on data collected from mobile telephones are reported; they demonstrate the benefits of the proposed scheme.

Fusion of local and regional approaches for on-line signature verification

Function-based methods for on-line signature verification are studied. These methods are classified into local and regional depending on the features used for matching. One representative method of each class is selected from the literature. The selected local and regional methods are based on Dynamic Time Warping and Hidden Markov Models, respectively. Some improvements are presented for the local method aimed at strengthening the performance against skilled forgeries. The two methods are compared following the protocol defined in the Signature Verification Competition 2004. Fusion results are also provided demonstrating the complementary nature of these two approaches.

On the Vulnerability of Fingerprint Verification Systems to Fake Fingerprints Attacks

A new method to generate gummy fingers is presented. A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it. Three different scenarios are considered in the experiments, namely: enrollment and test with real fingerprints, enrollment and test with fake fingerprints, and enrollment with real fingerprints and test with fake fingerprints. Results for optical and thermal sweeping sensors are given. Both systems are shown to be vulnerable to direct attacks

Kernel-based multimodal biometric verification using quality signals

A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-off coefficients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.