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20 results on '"BENLAMOUDI, Azeddine"'

3. Facial Age Estimation Using Multi-Stage Deep Neural Networks.

Author

Bekhouche, Salah Eddine, Benlamoudi, Azeddine, Dornaika, Fadi, Telli, Hichem, and Bounab, Yazid

Subjects
ARTIFICIAL neural networks, COMPUTER vision, ARTIFICIAL intelligence, SOCIAL media, LAW enforcement
Abstract

Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning technology. Notably, computer vision solutions have significantly contributed to these achievements. Human face analysis, a core area of computer vision, has gained considerable attention due to its wide applicability in fields such as law enforcement, social media, and marketing. However, existing methods for facial age estimation often struggle with accuracy due to limited feature extraction capabilities and inefficiencies in learning hierarchical representations. This paper introduces a novel framework to address these issues by proposing a Multi-Stage Deep Neural Network (MSDNN) architecture. The MSDNN architecture divides each CNN backbone into multiple stages, enabling more comprehensive feature extraction, thereby improving the accuracy of age predictions from facial images. Our framework demonstrates a significant performance improvement over traditional solutions, with its effectiveness validated through comparisons with the EfficientNet and MobileNetV3 architectures. The proposed MSDNN architecture achieves a notable decrease in Mean Absolute Error (MAE) across three widely used public datasets (MORPH2, CACD, and AFAD) while maintaining a virtually identical parameter count compared to the initial backbone architectures. These results underscore the effectiveness and feasibility of our methodology in advancing the field of age estimation, showcasing it as a robust solution for enhancing the accuracy of age prediction algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Facial age estimation using BSIF and LBP

Author

Bekhouche, Salah Eddine, Ouafi, Abdelkrim, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, and Benlamoudi, Azeddine

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database., Comment: 5 pages, 8 figures

Published
2016
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8. SNCF workers detection in the railway environment based on improved YOLO v5

Author

Hathat, Yahia, Samai, Djamel, Benlamoudi, Azeddine, Bensid, Khaled, Taleb-Ahmed, Abdelmalik, Université Kasdi Merbah Ouargla, Center for Machine Vision Research (CMV), University of Oulu, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and no information

Subjects
[SPI]Engineering Sciences [physics], YOLO v5, convolutional neural network, object detection, SNCF workers
Abstract

International audience; In nearest past years object detection techniques becomes the magic key to solving several problems in computer vision, in this work, we introduce our enhanced YOLO v5 detector for detecting SNCF (National Society of France Railroad) workers in the railway environment. Our contribution in this work is presented by creating a new dataset about SNCF workers to use for training our model detector and improving YOLO v5 by reducing the number of its parameters where we reduce the number of classes in YOLO layers to only one class, that ensure to augment the speed of detection and increase the accuracy of our detector. Finally, we apply the four versions of YOLO v5 (S, M, L, X) and compare them. We achieved a high speed in the detection of SNCF workers in YOLO v5-S with 0.1 ms and high precision in YOLO v5-X with a rate of 0.9731 %.

Published
2022

12. EFFICIENT COVID-19 DISEASE DIAGNOSIS BASED ON COUGH SIGNAL PROCESSING AND SUPERVISED MACHINE LEARNING.

Author

BENSID, Khaled, LATI, Abdelhai, BENLAMOUDI, Azeddine, GHOUAR, Brahim Elkhalil, and SENOUSSI, Mohammed Larbi

Subjects
COVID-19, DIAGNOSIS, COVID-19 testing, SIGNAL processing, GLOBAL Financial Crisis, 2008-2009
Abstract

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation; second, cough signal extraction; and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), KNearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Face spoofing detection using Multi-Level Local Phase Quantization (ML-LPQ)

Author

Benlamoudi, Azeddine, Samai, Djamel, Ouafi, Abdelkrim, Bekhouche, Salah Eddine, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), and Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
"spoofing", 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, "CASIA", "ML-LPQ", "biometrics", "LibSVM", [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Biometric technologies are becoming the foundationof an extensive array of highly secure identification and verifica-tion solution. Unfortunately, biometric systems are vulnerable toattacks made by persons showings photo, video or mask to spoofthe real identity. In this paper we study a solution for those prob-lems. We try to make solution to face spoofing for distinguishingbetween real face and fake one. Our approach called Multi-LevelLocal Phase Quantization (ML-LPQ) is focused in Local PhaseQuantization (LPQ) descriptor for extracting features on faceregion of interest. In our approach, we use three levels for theLPQ descriptor to extract features and LibSVM for classification.Our experimental analysis on a publicly available CASIA faceanti-spoofing database give us good result compared to otherapproaches using the same protocol

Published
2015

17. AUTOMATIC AGE ESTIMATION AND GENDER CLASSIFICATION IN THE WILD

Author

Bekhouche, Salah Eddine, Ouafi, Abdelkrim, Benlamoudi, Azeddine, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), and Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
"Gender classification", "Age estimation", "Local Binary Pattern", "Support Vector Machines", [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Automatic age estimation and gender classification throughfacial images are attractive topics in computer vision. Theycan be used in many real-life applications such as face recog-nition and internet safety for minors. In this paper, we presenta novel approach for age estimation and gender classificationunder uncontrolled conditions following the standard proto-cols for fair comparaison. Our proposed approach is based onMulti Level Local Binary Pattern (ML-LBP) features whichare extracted from normalized face images. Two differentSupport Vector Machines (SVM) models are used to predictthe age group and the gender of a person. The experimen-tal results on benchmark Image of Groups dataset showed thesuperiority of our approach compared to that of the state-of-the-art methods

Published
2015
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18. Facial age estimation using BSIF and LBP

Author

Bekhouche, Salah Eddine, Ouafi, Abdelkrim, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Benlamoudi, Azeddine, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Center for Machine Vision Research (CMV), and University of Oulu

Subjects
FOS: Computer and information sciences, "LBP", "Regression", Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, "Age estimation", 020207 software engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, "BSIF", [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

Human face aging is irreversible process causing changes in human face characteristics such us hair whitening, muscles drop and wrinkles. Due to the importance of human face aging in biometrics systems, age estimation became an attractive area for researchers. This paper presents a novel method to estimate the age from face images, using binarized statistical image features (BSIF) and local binary patterns (LBP)histograms as features performed by support vector regression (SVR) and kernel ridge regression (KRR). We applied our method on FG-NET and PAL datasets. Our proposed method has shown superiority to that of the state-of-the-art methods when using the whole PAL database., 5 pages, 8 figures

Published
2014
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