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Tunis, Tunisia

Balti A.,SICISI Unit | Sayadi M.,SICISI Unit
Journal of Electrical Engineering | Year: 2013

This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutia's. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutia points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase. Whatever, the degree of fingerprint rotation, the extraction features used to characterize the fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate position of fingerprint minutia's. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. After that, the classification accuracy of the proposed approach is evaluated with Back Propagation Neural Network (BPNN). Extensive experiments prove that the fingerprint classification based on a novel features and BPNN classifier give better results in fingerprint classification than several other features and methods. Source


Balti A.,SICISI Unit | Sayadi M.,SICISI Unit | Fnaiech F.,University of Picardie Jules Verne
Control Engineering and Applied Informatics | Year: 2013

This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutiae's. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutiae points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase. Whatever, the degree of fingerprint rotation, the extraction features used to characterize fingerprint remains the same. The characterization efficiency of the proposed method is compared to the method based on the spatial coordinate of fingerprint minutiae's. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. After that, the classification accuracy of the proposed approach is evaluated with Back Propagation Neural Network (BPNN). Extensive experiments prove that the fingerprint classification based on a novel features and BPNN classifier gives better results in fingerprint classification than several other features and methods. Finally the results of the proposed method are evaluated on the FVC 2002 database. Source


Balti A.,SICISI Unit | Sayadi M.,SICISI Unit | Fnaiech F.,SICISI Unit | Fnaiech F.,University of Picardie Jules Verne
2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013 | Year: 2013

This paper is concerned with novel features for fingerprint classification based on the Euclidian distance between the center point and their nearest neighbor bifurcation minutia's. The main advantage of the new method is the dimension reduction of the features vectors used to characterize fingerprint, compared with the classic characterization method based on the relative position of bifurcation minutia points. In addition, this new method avoids the problem of geometric rotation and translation over the acquisition phase. The characterization efficiency of the proposed method is compared with the method based on the spatial coordinate position of fingerprint minutia's. The comparison is based on a characterization criterion, usually used to evaluate the class quantification and the features discriminating ability. After that, the classification accuracy of the proposed approach is evaluated with Back Propagation Neural Network (BPNN). Extensive experiments prove that the Fingerprint classification based on a novel features and BPNN classifier give better results in fingerprint classification than several other features and methods. © 2013 IEEE. Source


Ben Chaabane S.,SICISI Unit | Sayadi M.,SICISI Unit | Sayadi M.,University of Picardie Jules Verne | Fnaiech F.,SICISI Unit | And 2 more authors.
Eurasip Journal on Advances in Signal Processing | Year: 2010

A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation. © 2010 Salim Ben Chaabane et al. Source


Marzouki A.,SICISI Unit | Hamouda M.,SICISI Unit | Fnaiech F.,SICISI Unit
IEEE International Symposium on Industrial Electronics | Year: 2010

The main objective of this paper is to implement a nonlinear control for a PWM voltage source converter with a reduced number of sensors. For this purpose an input-output feedback linearization control strategy is firstly implemented in order to control both the line current and the DC output voltage. Next, an estimator of the AC mains voltage is implemented so as to reduce the number of the requested sensors, to avoid the undesired noise, and to minimize the converter's cost. The efficiency of the proposed control law and estimation method is next validated through simulation results. Consequently, load independent unity input power factor operation and a perfect tracking of the DC bus voltage waveform are achieved even without using mains voltage sensors. © 2010 IEEE. Source

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