Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

Chater, Ahmed; Benradi, Hicham; Lasfar, Abdelali

doi:10.11591/ijece.v12i2.pp1429-1436

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…It eliminates the redundant computations performed by batch gradient descent in the case of large data sets which recalculates the gradients before each parameter update and performs one update at a time using the following formula, 𝜃 = 𝜃 − 𝜂. 𝛻 𝜃 𝑗(𝜃; 𝑥 (𝑖) ; 𝑦 (𝑖) ) (16) Where: θ : model parameters ; 𝜂: learning rate ; ∇θJ(θ) : objective function 𝑥 (𝑖) : training set; label…”

Section: Model Training Using a Convolutional Neural Network Cnnmentioning

confidence: 99%

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Benradi

Chater

Lasfar

2023

IJ-AI

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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%.

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Section: Model Training Using a Convolutional Neural Network Cnnmentioning

confidence: 99%

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Benradi

Chater

Lasfar

2023

IJ-AI

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“…extraction and classification such as [16][17][18]. CNNs are a category of deep neural networks used mainly in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

A combined method based on CNN architecture for variation-resistant facial recognition

Benradi,

Chater,

Lasfar

2023

Int. j. electr. comput. eng. syst. (Online)

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Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

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“…The field of medical imaging has been revolutionized by the integration of adapted image processing techniques from the field of computer vision [1] [2] [3] [4] [5]. Among these techniques, the use of convolutional neural networks (CNNs) is widely used in the field of deep learning.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Approach Lung Diseases and COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks: A Promising Approach for Accurate Diagnosis

Benradi,

Bouganssa,

Chater

et al. 2023

Int. J. Onl. Eng.

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Medical imaging treatment is one of the best-known computer science disciplines. It can be used to detect the presence of several diseases such as skin cancer and brain tumors, and since the arrival of the coronavirus (COVID-19), this technique has been used to alleviate the heavy burden placed on all health institutions and personnel, given the high rate of spread of this virus in the population. One of the problems encountered in diagnosing people suspected of having contracted COVID-19 is the difficulty of distinguishing symptoms due to this virus from those of other diseases such as influenza, as they are similar. This paper proposes a new approach to distinguishing between lung diseases and COVID-19 by analyzing chest x-ray images using a convolutional neural network (CNN) architecture. To achieve this, pre-processing was carried out on the dataset using histogram equalization, and then we trained two sub-datasets from the dataset using the Train et Test, the first to be used in the training phase and the second to be used in the model validation phase. Then a CNN architecture composed of several convolution layers and fully connected layers was deployed to train our model. Finally, we evaluated our model using two different metrics: the confusion matrix and the receiver operating characteristic. The simulation results recorded are satisfactory, with an accuracy rate of 96.27%.

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Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

Cited by 3 publications

References 25 publications

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

A combined method based on CNN architecture for variation-resistant facial recognition

Discriminative Approach Lung Diseases and COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks: A Promising Approach for Accurate Diagnosis

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