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Najoua Essoukri Ben Amara
LATIS-Laboratory of Advanced Technology and Intelligent Systems, ENISo-National Engineering School of Sousse, Sousse University, Sousse, Tunisia

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Short Biography

Najoua Essoukri Ben Amara received a B.Sc., M.S., Ph.D., and HDR 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, and 2004, respectively. She became senior lecturer in July 2004 and full Professor in October 2009 in Electrical Engineering at the National School of Engineers of Sousse ENISo, University of Sousse, Tunisia. She is a founding member and director of the research group SAGE (Systèmes Avancés en Genie Electrique). From July 2008 to July 2011, she was the director of ENISo. She has a wide national and international visibility, as the chair of several national committees and chair/co-chair of various international scientific conferences. She acts in many program committees and belongs to several scientific ones. She was the coordinator of several European projects (Euromed 3+3 and Tempus). She initiated multiple collaborations with international research laboratories and socio-economic organizations. Since 2011, she is the President of the Tunisian Association of Innovative Techniques of Sousse. Her areas of research include pattern recognition, document analysis, multimodal biometrics, medical image processing, and computer vision, with applications to the segmentation of documents, biometric recognition, individuals, and detection/monitoring multi-object and authored/co-authored over 100 articles in various national publications.

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Special issue paper
Published: 11 June 2021 in International Journal on Document Analysis and Recognition (IJDAR)
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One of the most important preliminary tasks in a transcription system of historical document images is text line segmentation. Nevertheless, this task remains complex due to the idiosyncrasies of ancient document images. In this article, we present a complete framework for text line segmentation in historical Arabic or Latin document images. A two-step procedure is described. First, a deep fully convolutional networks (FCN) architecture has been applied to extract the main area covering the text core. In order to select the highest performing FCN architecture, a thorough performance benchmarking of the most recent and widely used FCN architectures for segmenting text lines in historical Arabic or Latin document images has been conducted. Then, a post-processing step, which is based on topological structure analysis is introduced to extract complete text lines (including the ascender and descender components). This second step aims at refining the obtained FCN results and at providing sufficient information for text recognition. Our experiments have been carried out using a large number of Arabic and Latin document images collected from the Tunisian national archives as well as other benchmark datasets. Quantitative and qualitative assessments are reported in order to firstly pinpoint the strengths and weaknesses of the different FCN architectures and secondly to illustrate the effectiveness of the proposed post-processing method.

ACS Style

Olfa Mechi; Maroua Mehri; Rolf Ingold; Najoua Essoukri Ben Amara. A two-step framework for text line segmentation in historical Arabic and Latin document images. International Journal on Document Analysis and Recognition (IJDAR) 2021, 24, 197 -218.

AMA Style

Olfa Mechi, Maroua Mehri, Rolf Ingold, Najoua Essoukri Ben Amara. A two-step framework for text line segmentation in historical Arabic and Latin document images. International Journal on Document Analysis and Recognition (IJDAR). 2021; 24 (3):197-218.

Chicago/Turabian Style

Olfa Mechi; Maroua Mehri; Rolf Ingold; Najoua Essoukri Ben Amara. 2021. "A two-step framework for text line segmentation in historical Arabic and Latin document images." International Journal on Document Analysis and Recognition (IJDAR) 24, no. 3: 197-218.

Journal article
Published: 30 April 2021 in Traitement du Signal
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Point cloud-based Deep Neural Networks (DNNs) have gained increasing attention as an insightful solution in the study field of geometric deep learning. Point set aware DNNs have proven capable of dealing with the unstructured data type and successful in 3D data applications such as 3D object classification, segmentation and recognition. On the other hand, two major challenges remain understudied when it comes to the use of point cloud-based DNNs for 3D facial expression (FE) recognition. The first challenge is the lack of large labelled 3D facial data. The second is how to obtain a point-based discriminative representation of 3D faces. To address the first issue, we suggest to enlarge the used dataset by generating synthetic 3D FEs. For the second one, we propose to apply a level-curve based sampling strategy in order to exploit crucial geometric information. The conducted experiments show promising results reaching 97.23% on the enlarged BU-3DFE dataset.

ACS Style

Imen Hamrouni Trimech; Ahmed Maalej; Najoua Essoukri Ben Amara. Facial Expression Recognition Using 3D Points Aware Deep Neural Network. Traitement du Signal 2021, 38, 321 -330.

AMA Style

Imen Hamrouni Trimech, Ahmed Maalej, Najoua Essoukri Ben Amara. Facial Expression Recognition Using 3D Points Aware Deep Neural Network. Traitement du Signal. 2021; 38 (2):321-330.

Chicago/Turabian Style

Imen Hamrouni Trimech; Ahmed Maalej; Najoua Essoukri Ben Amara. 2021. "Facial Expression Recognition Using 3D Points Aware Deep Neural Network." Traitement du Signal 38, no. 2: 321-330.

Journal article
Published: 18 January 2021 in Informatics
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Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset.

ACS Style

Sameh Neili Boualia; Najoua Essoukri Ben Amara. Deep Full-Body HPE for Activity Recognition from RGB Frames Only. Informatics 2021, 8, 2 .

AMA Style

Sameh Neili Boualia, Najoua Essoukri Ben Amara. Deep Full-Body HPE for Activity Recognition from RGB Frames Only. Informatics. 2021; 8 (1):2.

Chicago/Turabian Style

Sameh Neili Boualia; Najoua Essoukri Ben Amara. 2021. "Deep Full-Body HPE for Activity Recognition from RGB Frames Only." Informatics 8, no. 1: 2.

Journal article
Published: 27 March 2020 in Electronics
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A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

ACS Style

Amira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics 2020, 9, 560 .

AMA Style

Amira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics. 2020; 9 (4):560.

Chicago/Turabian Style

Amira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. 2020. "OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception." Electronics 9, no. 4: 560.

Journal article
Published: 25 February 2019 in Future Generation Computer Systems
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Biometric authentication systems are increasingly considered in different access control applications. Regarding that users have completely different interactions with these authentication systems, several techniques have been developed in the literature to model distinctive users categories. Doddington zoo is a biometric menagerie that defines and labels user groups with animal species to reflect their behavior with the biometric systems. This menagerie was developed for different biometric modalities including keystroke dynamics. The present study proposes a user dependent adaptive strategy based on the Doddinghton zoo, for the recognition of the user’s keystroke dynamics. The novelty of the proposed approach lies in applying an adaptive strategy specific to the characteristics of each user of the Doddinghton zoo menagerie aiming to solve the intra-class variation problems. The obtained results demonstrate competitive performances on significant keystroke dynamics datasets WEBGREYC and CMU.

ACS Style

Abir Mhenni; Estelle Cherrier; Christophe Rosenberger; Najoua Essoukri Ben Amara. Analysis of Doddington zoo classification for user dependent template update: Application to keystroke dynamics recognition. Future Generation Computer Systems 2019, 97, 210 -218.

AMA Style

Abir Mhenni, Estelle Cherrier, Christophe Rosenberger, Najoua Essoukri Ben Amara. Analysis of Doddington zoo classification for user dependent template update: Application to keystroke dynamics recognition. Future Generation Computer Systems. 2019; 97 ():210-218.

Chicago/Turabian Style

Abir Mhenni; Estelle Cherrier; Christophe Rosenberger; Najoua Essoukri Ben Amara. 2019. "Analysis of Doddington zoo classification for user dependent template update: Application to keystroke dynamics recognition." Future Generation Computer Systems 97, no. : 210-218.

Journal article
Published: 08 February 2019 in Computers & Security
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Cyber-attacks have spread all over the world to steal information such as trade secrets, intellectual property and banking data. Facing the danger of the insecurity of saved data (personal, professional, official, etc), keystroke dynamics was proposed as an interesting, non-intrusive, inexpensive, permanent and weakly constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a single-capture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.

ACS Style

Abir Mhenni; Estelle Cherrier; Christophe Rosenberger; Najoua Essoukri Ben Amara. Double serial adaptation mechanism for keystroke dynamics authentication based on a single password. Computers & Security 2019, 83, 151 -166.

AMA Style

Abir Mhenni, Estelle Cherrier, Christophe Rosenberger, Najoua Essoukri Ben Amara. Double serial adaptation mechanism for keystroke dynamics authentication based on a single password. Computers & Security. 2019; 83 ():151-166.

Chicago/Turabian Style

Abir Mhenni; Estelle Cherrier; Christophe Rosenberger; Najoua Essoukri Ben Amara. 2019. "Double serial adaptation mechanism for keystroke dynamics authentication based on a single password." Computers & Security 83, no. : 151-166.

Journal article
Published: 31 January 2018 in Journal of Imaging
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Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

ACS Style

Oussama Zayene; Sameh Masmoudi Touj; Jean Hennebert; Rolf Ingold; Najoua Essoukri Ben Amara. Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames. Journal of Imaging 2018, 4, 32 .

AMA Style

Oussama Zayene, Sameh Masmoudi Touj, Jean Hennebert, Rolf Ingold, Najoua Essoukri Ben Amara. Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames. Journal of Imaging. 2018; 4 (2):32.

Chicago/Turabian Style

Oussama Zayene; Sameh Masmoudi Touj; Jean Hennebert; Rolf Ingold; Najoua Essoukri Ben Amara. 2018. "Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames." Journal of Imaging 4, no. 2: 32.

Research article
Published: 18 August 2017 in IET Computer Vision
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This study addresses the problem of efficiently combining the joint, RGB and depth modalities of the Kinect sensor in order to recognise human actions. For this purpose, a multi-layered fusion scheme concatenates different specific features, builds specialised local and global SVM models and then iteratively fuses their different scores. The authors essentially contribute in two levels: (i) they combine the performance of local descriptors with the strength of global bags-of-visual-words representations. They are able then to generate improved local decisions that allow noisy frames handling. (ii) They also study the performance of multiple fusion schemes guided by different features concatenations, Fisher vectors representations concatenation and later iterative scores fusion. To prove the efficiency of their approach, they have evaluated their experiments on two challenging public datasets: CAD-60 and CGC-2014. Competitive results are obtained for both benchmarks.

ACS Style

Bassem Seddik; Sami Gazzah; Najoua Essoukri Ben Amara. Human‐action recognition using a multi‐layered fusion scheme of Kinect modalities. IET Computer Vision 2017, 11, 530 -540.

AMA Style

Bassem Seddik, Sami Gazzah, Najoua Essoukri Ben Amara. Human‐action recognition using a multi‐layered fusion scheme of Kinect modalities. IET Computer Vision. 2017; 11 (7):530-540.

Chicago/Turabian Style

Bassem Seddik; Sami Gazzah; Najoua Essoukri Ben Amara. 2017. "Human‐action recognition using a multi‐layered fusion scheme of Kinect modalities." IET Computer Vision 11, no. 7: 530-540.

Journal article
Published: 31 May 2017 in Energies
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Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

ACS Style

Ines Baccouche; Sabeur Jemmali; Bilal Manai; Noshin Omar; Najoua Essoukri Ben Amara. Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter. Energies 2017, 10, 764 .

AMA Style

Ines Baccouche, Sabeur Jemmali, Bilal Manai, Noshin Omar, Najoua Essoukri Ben Amara. Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter. Energies. 2017; 10 (6):764.

Chicago/Turabian Style

Ines Baccouche; Sabeur Jemmali; Bilal Manai; Noshin Omar; Najoua Essoukri Ben Amara. 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter." Energies 10, no. 6: 764.

Conference paper
Published: 21 August 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
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We propose in this work an approach for the automatic extraction and recognition of the Italian sign language using the RGB, depth and skeletal-joint modalities offered by Microsoft’s Kinect sensor. We investigate the best modality combination that improves the human-action spotting and recognition in a continuous stream scenario. For this purpose, we define per modality a complementary feature representation and fuse the decisions of multiple SVM classifiers with probability outputs. We contribute by proposing a multi-scale analysis approach that combines a global Fisher vector representation with a local frame-wise one. In addition we define a temporal segmentation strategy that allows the generation of multiple specialized classifiers. The final decision is obtained using the combination of their results. Our tests have been carried out on the Chalearn gesture challenge dataset, and promising results have been obtained on primary experiments.

ACS Style

Bassem Seddik; Sami Gazzah; Najoua Essoukri Ben Amara. Modalities Combination for Italian Sign Language Extraction and Recognition. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 710 -721.

AMA Style

Bassem Seddik, Sami Gazzah, Najoua Essoukri Ben Amara. Modalities Combination for Italian Sign Language Extraction and Recognition. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():710-721.

Chicago/Turabian Style

Bassem Seddik; Sami Gazzah; Najoua Essoukri Ben Amara. 2015. "Modalities Combination for Italian Sign Language Extraction and Recognition." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 710-721.

Conference paper
Published: 29 May 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this study, we propose a synchronous Multi-Stream Hidden Markov Model (MSHMM) for offline Arabic handwriting word recognition. Our proposed model has the advantage of efficiently modelling the temporal interaction between multiple features. These features are composed of a combination of statistical and structural ones, which are extracted over the columns and rows using a sliding window approach. In fact, word models are implemented based on the holistic and analytical approaches without any explicit segmentation. In the first approach, all the words share the same architecture but the parameters are different. Nevertheless, in the second approach, each word has it own model by concatenating its character models. The results carried out on the IFN/ENIT database show that the analytical approach performs better than the holistic one and the MSHMMs in Arabic handwriting recognition is reliable.

ACS Style

Khaoula Jayech; Mohamed Ali Mahjoub; Najoua Essoukri Ben Amara. Arabic Handwriting Recognition Based on Synchronous Multi-stream HMM Without Explicit Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 136 -145.

AMA Style

Khaoula Jayech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara. Arabic Handwriting Recognition Based on Synchronous Multi-stream HMM Without Explicit Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():136-145.

Chicago/Turabian Style

Khaoula Jayech; Mohamed Ali Mahjoub; Najoua Essoukri Ben Amara. 2015. "Arabic Handwriting Recognition Based on Synchronous Multi-stream HMM Without Explicit Segmentation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 136-145.

Journal article
Published: 05 September 2014 in International Journal of Computational Intelligence Systems
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Case retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this context, we suggest in this paper a similarity measure applied to brain tumor cases retrieval. The rationale behind the proposed measure consists in quantifying the diagnosis correspondence followed by a clinician while comparing two medical cases. Our idea is characterized by the use of the Bayesian inference in the formulation of the proposed measure. The Bayesian network is applied in the classification task and it describes the decision-making process of a radiologist facing a tumor. The proposed similarity algorithm is based essentially on graph correspondence based on signature nodes comparison from the Bayesian classifiers. experiments were directed to compare the performance of the proposed similarity measure method with classical methods of similarity quantification. The performance indices of our proposition are promising.

ACS Style

Hedi Yazid; Karim Kalti; Najoua Essoukri Benamara. A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval. International Journal of Computational Intelligence Systems 2014, 7, 1123 -1136.

AMA Style

Hedi Yazid, Karim Kalti, Najoua Essoukri Benamara. A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval. International Journal of Computational Intelligence Systems. 2014; 7 (6):1123-1136.

Chicago/Turabian Style

Hedi Yazid; Karim Kalti; Najoua Essoukri Benamara. 2014. "A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval." International Journal of Computational Intelligence Systems 7, no. 6: 1123-1136.

Journal article
Published: 16 June 2013 in International Journal of Image, Graphics and Signal Processing
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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

ACS Style

Anouar Ben Khalifa; Lamia Rzouga; Najoua Essoukri Benamara. Wavelet, Gabor Filters and Co-occurrence Matrix for Palmprint Verification. International Journal of Image, Graphics and Signal Processing 2013, 5, 1 -8.

AMA Style

Anouar Ben Khalifa, Lamia Rzouga, Najoua Essoukri Benamara. Wavelet, Gabor Filters and Co-occurrence Matrix for Palmprint Verification. International Journal of Image, Graphics and Signal Processing. 2013; 5 (8):1-8.

Chicago/Turabian Style

Anouar Ben Khalifa; Lamia Rzouga; Najoua Essoukri Benamara. 2013. "Wavelet, Gabor Filters and Co-occurrence Matrix for Palmprint Verification." International Journal of Image, Graphics and Signal Processing 5, no. 8: 1-8.