Publications Scientifiques

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    Identity recognition based on palmprints : the preliminary results
    (IEEE, 2022) Amrouni, Nadia; Benzaoui, Amir; Adjabi, Insaf
    Private and automatic recognition in many applications, such as forensic, access control, and surveillance systems, has become necessary in recent years. Biometrics, which treats individuals' identification based on physical or behavioral characteristics, has emerged as an effective automated identification technology, offering more properties and advantages than conventional protection. The use of palmprints in biometric authentication has dramatically increased and has been used extensively in management systems for businesses, Internet of Thinks, and individuals. In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure. As part of this work, the local binary pattern descriptor (LBP) was used and tested under several configurations to extract the palmprint modality's optimal and efficient characteristics. As preliminary results, our experiments on the IITD Palmprint V1 database exhibit impressive performance
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    A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative
    (Springer, 2022) Messaoudene, Khadidja; Harrar, Khaled
    Knee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literature
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    Combining GLCM with LBP features for knee osteoarthritis prediction: data from the osteoarthritis initiative
    (EAI, 2021) Harrar, Khaled; Messaoudene, Khadidja; Ammar, Mohammed
    Knee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these complications. OBJECTIVES: This paper introduces a quick and effective approach to classify knee X-ray images using LogitBoost and wavelet-based Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) to increase image classification accuracy and minimize training and testing time. METHODS: The proposed technique involves image enhancement followed by Haar wavelet transformation. GLCM and LBP were extracted from the transformed image and these attributes were used to differentiate the radiographs into two groups of patients composed of 100 normal subjects (KL 0) and 100 pathological cases with osteoarthritis (KL 2). The validation of the classification was carried out using the K-fold cross-validation technique with k = 10. RESULTS: The results revealed that the GLCM provided an accuracy of 77 % and the LBP approach achieved an accuracy of 82.5 %. Moreover, the combination of the two techniques LBP-GLCM improved the accuracy of the prediction with the LogitBoost model (91.16 %). Compared to other classifiers (SVM, logistic regression, and decision tree), the LogitBoost provided a low root mean square error (RMSE) of 27.5 %. CONCLUSION: In addition, the proposed method was compared to the state-of-the-art and revealed the highest accuracy in the prediction of KOA, outperforming the methods existing in the literature
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    Fusion of face recognition methods at score level
    (IEEE, 2017) Cherifi, Dalila; Cherfaoui, Fateh; Yacini, Si Nabil; Nait-Ali, Amine