Feature extraction and information fusion in face and palmprint multimodal biometrics

ThesisMultimodal biometric systems that integrate the biometric traits from several modalities are able to overcome the limitations of single modal biometrics. Fusing the information at an earlier level by consolidating the features given by different traits can give a better result due to the richness of information at this stage. In this thesis, three novel methods are derived and implemented on face and palmprint modalities, taking advantage of the multimodal biometric fusion at feature level. The benefits of the proposed method are the enhanced capabilities in discriminating information in the fused features and capturing all of the information required to improve the classification performance. Multimodal biometric proposed here consists of several stages such as feature extraction, fusion, recognition and classification. Feature extraction gathers all important information from the raw images. A new local feature extraction method has been designed to extract information from the face and palmprint images in the form of sub block windows. Multiresolution analysis using Gabor transform and DCT is computed for each sub block window to produce compact local features for the face and palmprint images. Multiresolution Gabor analysis captures important information in the texture of the images while DCT represents the information in different frequency components. Important features with high discrimination power are then preserved by selecting several low frequency coefficients in order to estimate the model parameters. The local features extracted are fused in a new matrix interleaved method. The new fused feature vector is higher in dimensionality compared to the original feature vectors from both modalities, thus it carries high discriminating power and contains rich statistical information. The fused feature vector also has larger data points in the feature space which is advantageous for the training process using statistical methods. The underlying statistical information in the fused feature vectors is captured using GMM where several numbers of modal parameters are estimated from the distribution of fused feature vector. Maximum likelihood score is used to measure a degree of certainty to perform recognition while maximum likelihood score normalization is used for classification process. The use of likelihood score normalization is found to be able to suppress an imposter likelihood score when the background model parameters are estimated from a pool of users which include statistical information of an imposter. The present method achieved the highest recognition accuracy 97% and 99.7% when tested using FERET-PolyU dataset and ORL-PolyU dataset respectively.Universiti Malaysia Perlis and Ministry of Higher Education Malaysi

Analyse de la qualité des signatures manuscrites en-ligne par la mesure d'entropie

Cette thèse s'inscrit dans le contexte de la vérification d'identité par la signature manuscrite en-ligne. Notre travail concerne plus particulièrement la recherche de nouvelles mesures qui permettent de quantifier la qualité des signatures en-ligne et d'établir des critères automatiques de fiabilité des systèmes de vérification. Nous avons proposé trois mesures de qualité faisant intervenir le concept d entropie. Nous avons proposé une mesure de qualité au niveau de chaque personne, appelée Entropie personnelle , calculée sur un ensemble de signatures authentiques d une personne. L originalité de l approche réside dans le fait que l entropie de la signature est calculée en estimant les densités de probabilité localement, sur des portions, par le biais d un Modèle de Markov Caché. Nous montrons que notre mesure englobe les critères habituels utilisés dans la littérature pour quantifier la qualité d une signature, à savoir: la complexité, la variabilité et la lisibilité. Aussi, cette mesure permet de générer, par classification non supervisée, des catégories de personnes, à la fois en termes de variabilité de la signature et de complexité du tracé. En confrontant cette mesure aux performances de systèmes de vérification usuels sur chaque catégorie de personnes, nous avons trouvé que les performances se dégradent de manière significative (d un facteur 2 au minimum) entre les personnes de la catégorie haute Entropie (signatures très variables et peu complexes) et celles de la catégorie basse Entropie (signatures les plus stables et les plus complexes). Nous avons ensuite proposé une mesure de qualité basée sur l entropie relative (distance de Kullback-Leibler), dénommée Entropie Relative Personnelle permettant de quantifier la vulnérabilité d une personne aux attaques (bonnes imitations). Il s agit là d un concept original, très peu étudié dans la littérature. La vulnérabilité associée à chaque personne est calculée comme étant la distance de Kullback-Leibler entre les distributions de probabilité locales estimées sur les signatures authentiques de la personne et celles estimées sur les imitations qui lui sont associées. Nous utilisons pour cela deux Modèles de Markov Cachés, l'un est appris sur les signatures authentiques de la personne et l'autre sur les imitations associées à cette personne. Plus la distance de Kullback-Leibler est faible, plus la personne est considérée comme vulnérable aux attaques. Cette mesure est plus appropriée à l analyse des systèmes biométriques car elle englobe en plus des trois critères habituels de la littérature, la vulnérabilité aux imitations. Enfin, nous avons proposé une mesure de qualité pour les signatures imitées, ce qui est totalement nouveau dans la littérature. Cette mesure de qualité est une extension de l Entropie Personnelle adaptée au contexte des imitations: nous avons exploité l information statistique de la personne cible pour mesurer combien la signature imitée réalisée par un imposteur va coller à la fonction de densité de probabilité associée à la personne cible. Nous avons ainsi défini la mesure de qualité des imitations comme étant la dissimilarité existant entre l'entropie associée à la personne à imiter et celle associée à l'imitation. Elle permet lors de l évaluation des systèmes de vérification de quantifier la qualité des imitations, et ainsi d apporter une information vis-à-vis de la résistance des systèmes aux attaques. Nous avons aussi montré l intérêt de notre mesure d Entropie Personnelle pour améliorer les performances des systèmes de vérification dans des applications réelles. Nous avons montré que la mesure d Entropie peut être utilisée pour : améliorer la procédure d enregistrement, quantifier la dégradation de la qualité des signatures due au changement de plateforme, sélectionner les meilleures signatures de référence, identifier les signatures aberrantes, et quantifier la pertinence de certains paramètres pour diminuer la variabilité temporelle.This thesis is focused on the quality assessment of online signatures and its application to online signature verification systems. Our work aims at introducing new quality measures quantifying the quality of online signatures and thus establishing automatic reliability criteria for verification systems. We proposed three quality measures involving the concept of entropy, widely used in Information Theory. We proposed a novel quality measure per person, called "Personal Entropy" calculated on a set of genuine signatures of such a person. The originality of the approach lies in the fact that the entropy of the genuine signature is computed locally, on portions of such a signature, based on local density estimation by a Hidden Markov Model. We show that our new measure includes the usual criteria of the literature, namely: signature complexity, signature variability and signature legibility. Moreover, this measure allows generating, by an unsupervised classification, 3 coherent writer categories in terms of signature variability and complexity. Confronting this measure to the performance of two widely used verification systems (HMM, DTW) on each Entropy-based category, we show that the performance degrade significantly (by a factor 2 at least) between persons of "high Entropy-based category", containing the most variable and the least complex signatures and those of "low Entropy-based category", containing the most stable and the most complex signatures. We then proposed a novel quality measure based on the concept of relative entropy (also called Kullback-Leibler distance), denoted Personal Relative Entropy for quantifying person's vulnerability to attacks (good forgeries). This is an original concept and few studies in the literature are dedicated to this issue. This new measure computes, for a given writer, the Kullback-Leibler distance between the local probability distributions of his/her genuine signatures and those of his/her skilled forgeries: the higher the distance, the better the writer is protected from attacks. We show that such a measure simultaneously incorporates in a single quantity the usual criteria proposed in the literature for writer categorization, namely signature complexity, signature variability, as our Personal Entropy, but also the vulnerability criterion to skilled forgeries. This measure is more appropriate to biometric systems, because it makes a good compromise between the resulting improvement of the FAR and the corresponding degradation of FRR. We also proposed a novel quality measure aiming at quantifying the quality of skilled forgeries, which is totally new in the literature. Such a measure is based on the extension of our former Personal Entropy measure to the framework of skilled forgeries: we exploit the statistical information of the target writer for measuring to what extent an impostor s hand-draw sticks to the target probability density function. In this framework, the quality of a skilled forgery is quantified as the dissimilarity existing between the target writer s own Personal Entropy and the entropy of the skilled forgery sample. Our experiments show that this measure allows an assessment of the quality of skilled forgeries of the main online signature databases available to the scientific community, and thus provides information about systems resistance to attacks. Finally, we also demonstrated the interest of using our Personal Entropy measure for improving performance of online signature verification systems in real applications. We show that Personal Entropy measure can be used to: improve the enrolment process, quantify the quality degradation of signatures due to the change of platforms, select the best reference signatures, identify the outlier signatures, and quantify the relevance of times functions parameters in the context of temporal variability.EVRY-INT (912282302) / SudocSudocFranceF