Generating One Biometric Feature from Another: Faces from Fingerprints

This study presents a new approach based on artificial neural networks for generating one biometric feature (faces) from another (only fingerprints). An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces

Identification de personnes par fusion de différentes modalités biométriques

This thesis contributes to the resolution of the problems which are related to the analysis of the biometric data outcome from the iris, the fingerprint and the fusion of these two modalities, for person identification. Thus, after the evaluation of those proposed biometric systems, we have shown that the multimodal biometric system based on iris and fingerprint outperforms both monomodal biometric systems based whatsoever on the iris or on the fingerprint.Cette thèse contribue essentiellement à la résolution des problèmes liés à l'analyse des données biométriques issues de l'iris, de l'empreinte digitale et de la fusion de ces deux modalités pour l'identification de personne. Ainsi, après l'évaluation des trois systèmes biométriques proposés, nous avons prouvé que le système biométrique multimodal basé sur l'iris et l'empreinte digitale est plus performant que les deux systèmes biométriques monomodaux basés que se soit sur l'iris ou sur l'empreinte digitale

Two-class Fingerprint matcher

We present a system for fingerprint verification that approaches the problem as a two-class pattern recognition problem. The features extracted by “FingerCode” are used to capture the ridge strength. This feature vector is then classified as genuine or impostor according to a novel approach to handle the fingerprint verification as a two-class problem. Moreover, we show that extracting the features from sub-images around the core permits to better represent the local information

Two-class Fingerprint matcher

none2We present a system for fingerprint verification that approaches the problem as a two-class pattern recognition problem. The features extracted by ''FingerCode'' are used to capture the ridge strength. This feature vector is then classified as genuine or impostor according to a novel approach to handle the fingerprint verification as a two-class problem. Moreover, we show that extracting the features from sub-images around the core permits to better represent the local information.noneA. LUMINI; L. NANNIA., Lumini; Nanni, Lori