Najoua Essoukri BenAmara

Work place: Research Unit of Advanced Systems in Electrical Engineering, National Engineering School of Sousse, Tunisia



Research Interests: Image Processing, Image Compression, Pattern Recognition


Najoua Essoukri BenAmara received the B.Sc., M.S., Ph.D. and HDR degrees in Electrical Engineering, Signal Processing, System Analysis and Pattern Recognition from the National School of Engineers of Tunis, University El Manar, Tunisia, in 1985, 1986, 1999, 2004 respectively. From 1985 to 1989, she was a researcher at the Regional Institute of Informatics Sciences and Telecommunications, Tunis, Tunisia. In September 1989, she joined the Electrical Engineering Department of the National School of Engineers of Monastir, University of Monastir, Tunisia, as an assistant professor. She becomes a senior lecturer in July 2004 and a Professor in October 2009 in Electrical Engineering at the National School of Engineers of Sousse-ENISo, University of Sousse, Tunisia. Since July 2008, she is the Director of the ENISo. Her research interests include mainly pattern recognition applied to Arabic documents, ancient image processing, compression, watermarking, segmentation, biometric and the use of stochastic models and hybrid approaches in the above domains.

Author Articles
Wavelet, Gabor Filters and Co-occurrence Matrix for Palmprint Verification

By Anouar Ben Khalifa Lamia Rzouga Najoua Essoukri BenAmara

DOI:, Pub. Date: 28 Jun. 2013

Authentication through the palmprint is a field of biometrics. Palmprint-based personal verification has quickly entered the biometric family. It has become increasingly popular in the recent years due to its ease of acquisition, reliability and high user acceptance. In this paper, we present an authentication system based on the palmprint. We are particularly interested in the feature extraction step. Three feature extraction techniques based on the discrete wavelet transform, the Gabor filters and the co-occurrence matrix are evaluated. The support vector machine is used for the classification step. The results have been validated on the PolyU database related to 400 users. The best results have been achieved with the wavelet decomposition.

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Contribution to the Fusion of Biometric Modalities by the Choquet Integral

By Anouar Ben Khalifa Najoua Essoukri BenAmara

DOI:, Pub. Date: 28 Sep. 2012

In multimodal biometrics, modalities can be robust against the authentication of certain people and weak for others. The conventional fusion techniques such as the Product, Mean, AND, OR and the Majority Voting do not take into account this kind of behaviour. In this paper, we propose a new approach to fusion procedures in the context of biometric authentication. The proposed method is based on the exploration of the Choquet integral that takes into account the interactions between the terms and people through fuzzy measures. The fuzzy measures, the ones we have proposed, are based on the number of confusion, the entropy and the uncertainty function. The results have been validated in two databases: the first one is virtual, which is based on synthetic scores and the second one on the biometric modalities which are: face, off-line handwriting and off-line signature. The achieved results demonstrate the robustness of our approaches and their adaptability.

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Other Articles