Improved Methods for Automatic Facial Expression Recognition

Echoukairi, Hassan; Ghmary, Mohamed El; Ziani, Said; Ouacha, Ali

doi:10.3991/ijim.v17i06.37031

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…These results surpass the performance reported in the referenced studies, indicating a significant improvement in the signal separation process. The results of this paper can be optimized by incorporating the learning methods and wavelet theory as reported in the references [10,11,12,13]. It is also noted that regularization methods can be effectively applied in the fields of control and energy, as indicated in the references [14,15,16,17,18].…”

Section: Ecg Recordingmentioning

confidence: 87%

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Ziani,

El Ghmary,

Achmad Rizal

2023

Int. J. Onl. Eng.

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This research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques.

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Section: Ecg Recordingmentioning

confidence: 87%

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Ziani,

El Ghmary,

Achmad Rizal

2023

Int. J. Onl. Eng.

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“…A computational model, which usually consists of numerous processing layers, would be able to learn about the data representations at various abstraction levels by using deep learning in CNN [15]. Such approaches have been proven to drastically improve the accuracy of various visual recognition systems [16]. Image classification is the process of taking an input image and then classifying the output image by the predetermined or desired type of characterization.…”

Section: Literature Reviewmentioning

confidence: 99%

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Saedan,

Kassim,

Abd Aziz

2024

Int. J. Interact. Mob. Technol.

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This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.

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“…The objective of these architectures is to train the machine to detect the presence of diseases in various types of medical images, such as x-rays, chest radiographs (CXRs), computerized tomography (CT) scans, magnetic resonance imaging (MRI) scans, and so forth. CNNs are used in many fields, including robotics [6], facial expressions [7], education [8] [9], and also in the medical field such as the detection of brain tumors [10] [11] [12], prostate cancer [13] [14], lung nodules [15], COVID-19 [16] [17], and breast cancer [18] [19].…”

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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Improved Methods for Automatic Facial Expression Recognition

Cited by 7 publications

References 25 publications

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

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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