Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.

<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>

The world was shaken by the arrival of the corona virus (COVID-19), which ravaged all countries and caused a lot of human and economic damage. The world activity has been totally stopped in order to stop this pandemic, but unfortunately until today the world knows the arrival of new wave of contamination among the population despite the implementation of several vaccines that have been made available to the countries of the world and this is due to the appearance of new variants. All variants of this virus have recorded a common symptom which is an infection in the respiratory tract. In this paper a new method of detection of the presence of this virus in patients was implemented based on deep learning using a deep learning model by convolutional neural network architecture (CNN) using a COVID-QU chest X- ray imaging database. For this purpose, a pre-processing was performed on all the images used, aiming at unifying the dimensions of these images and applying a histogram equalization for an equitable distribution of the intensity on the whole of each image. After the pre-processing phase we proceeded to the formation of two groups, the first Train is used in the training phase of the model and the second called Test is used for the validation of the model. Finally, a lightweight CNN architecture was used to train a model. The model was evaluated using two metrics which are the confusion matrix which includes the following elements (ACCURACY, SPECIFITY, PRESITION, SENSITIVITY, F1_SCORE) and Receiver Operating Characteristic (the ROC curve). The results of our simulations showed an improvement after using the histogram equalization technique in terms of the following metrics: ACCURACY 96.5%, SPECIFITY 98.60% and PRESITION 98.66%.

<p>Recognition systems have received a lot of attention because of their various<br />uses in people's daily lives, for example in robotic intelligence, smart cameras,<br />security surveillance or even criminal identification. Determining the<br />similarity of faces by different face variations is based on robust algorithms.<br />The validation of our experiment is done on two sets of data. In this paper, we<br />compare two facial recognition system techniques according to the<br />recognition rate and the average authentication time: in order to increase the<br />accuracy rate and decrease the processing time. our approach is based on<br />feature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.</p>