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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Gazzeh, Soulayma Douik, Ali ENISo University of Sousse Laboratory of Networked Objects Control and Communications Systems Sousse Tunisia

ISBN: (纸本)9781665451161

Visual perception and road scene understanding are critical components of autonomous driving (AD). Detecting objects and predicting surrounding road user behavior are important tasks for robust and safe driving systems. With the rise of Machine Learning (ML) and Deep Learning (DL) methods using convolutional neural networks (CNN) , research on road scene understanding and motion recognition have made significant progress. In this paper, a real-time recognition of pedestrian's intention to cross is presented as a step towards autonomous driving. Our approach is constructed using a real-Time visual pedestrian tracker based on a one-stage object detector that gave a fast and robust learning produces dynamic and static features as inputs of a linear classifier for predicting crossing behavior. We apply our approach to public real-life benchmark - JAAD dataset "Joint Attention in Autonomous Driving."The experimental results have shown that our proposed method is efficient and competitive with other approaches. Overall, our system achieves an accuracy of 92.88%. © 2022 IEEE.

关键词： Autonomous vehicles

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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning 27

Automatic Face Expression Neutralisation and Posture Correct...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center University of Monastir National Engineering School of Monastir Sahloul Belt Road 4054 Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

The performance of recognition systems can be significantly affected by various factors, with facial expression poses and lighting changes being the main confounding factors. In order to minimize their impact, we propose an intriguing method in this study that enables the generation of high-quality images specifically tailored to the target domain. Our objective is to utilize disentangled representation to effectively model the decomposition of data variations and generate neutral facial expression images with frontal posture and adaptive illumination. To achieve this, we incorporate 3D priors in the adversarial learning during the training process, simulating the generation of an analytical 3D face deformation as well as rendering operations. Additionally, we employ contrastive learning to control the disentanglement of the generated faces while preserving the essential properties of facial features. This technique enables us to learn an embedding space in which similar data samples are represented closely, while distinct samples are kept far apart from each other. Furthermore, we conduct an analysis of the learned latent space and introduce several other significant properties that enhance the reinforcement of factor disentanglement. These properties include an imitation learning algorithm, which facilitates the acquisition of meaningful patterns and characteristics. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： 3D contrastive learning disentanglement expression neutralization generative adversarial network

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FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning 28th

FFGAN: An Auto-supervised Approach for Frontal Face Generati...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wiem, Grina Ali, Douik National Engineering School of Monastir University of Monastir Rue Ibn Jazzar Monastir5035 Tunisia Networked Objects Control and Communication Systems Laboratory Sousse Technology Centre National School of Engineers of Sousse University of Sousse Sahloul Belt Road Sousse4054 Tunisia

This work proposes a novel generative model, FF-GAN (Frontal Face Generative Adversarial Network), for generating high-quality and diverse frontal faces. FF-GAN utilizes contrastive learning to effectively learn the underlying representation of frontal faces from unlabeled image datasets. This approach allows the model to capture the essential characteristics of frontal faces without the need for explicit pose annotations. We evaluate the performance of FF-GAN using established metrics like FID (Fréchet Inception Distance), IS (Inception Score), and SSIM (Structural Similarity Index Measure). The results demonstrate that FF-GAN achieves superior performance compared to existing methods, generating highly realistic and visually appealing frontal faces with exceptional structural coherence. This research contributes to the field of facial image generation by introducing an effective unsupervised learning approach based on contrastive learning for generating high-quality frontal faces. © 2024 The Authors.

关键词： Contrastive Learning

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Face Expression Neutralisation Using Improved Variational Autoencoder 6

Face Expression Neutralisation Using Improved Variational Au...

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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse National Engineering School of Monastir Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Pôle Technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9781665451161

There is a high demand for realistic facial expressions in modern computer graphics and multimedia research. Unfortunately, synthesizing face expressions takes time, effort, and hard labour since high naturalism of face expressions is required. Despite recent advances in synthesizing realistic facial images, current generative models conflict to catch more involved image types, potentially due to their latent space simplified architecture. Furthermore, the union of a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) by using usually the Adam optimizer has recently been studied and investigated in cutting-edge research. Hence, at tiem of learning the generator takes a long time and then overfit for a particular time instance. So, in this work, we will suggest integrating the VAE to GAN's architecture by using the Ranger optimizer into an unsupervised generative model that at once learns to encode, generate and compare dataset samples to bring out various real neutral faces in a reduced and optimal time. Next, we proposed the probability distributions called Kullback-Leibler Divergence (KLD) as an objective weighting scheme that helps us to measure just how much information we lose when we choose an approximation during training. Also, the Mean Square Error (MSE), the most commonly used loss function for regression is used as a metric of evaluation for the experiment tests. © 2022 IEEE.

关键词： Mean square error

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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning

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Procedia Computer Science 2023年 225卷 269-278页

作者： Grina Wiem Douik Ali University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 University of Monastir National Engineering School of Monastir Rue Ibn Jazzar Monastir 5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

关键词： generative adversarial network expression neutralization disentanglement 3D contrastive learning

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Comparison of RNN-Based Models for Sign Language Recognition and Translation

Comparison of RNN-Based Models for Sign Language Recognition...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Marah El Askri Riadh Bchir Hend Basly Fatma Ezahra Sayadi National Engineering School of Sousse (ENISO) Laboratory of Networked Objects Control and Communication Systems (NOCCS) University of Sousse Sousse Tunisia Faculty of Sciences of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The recognition of human activities using vision based methods has become a key area of interest in video analytics research. In the past years, several sophisticated deep learning (DL) algorithms have surfaced, designed to recognize complex human actions depicted in video streams. These approaches have exhibited remarkable competence in tasks related to video analytics. This paper explores the field in greater depth by centering its focus on Sign Language (SL) as a unique and complex form of human activity. SL plays is pivotal in facilitating communication for people who are deaf or hard of hearing, enabling them to interact and engage. The main idea is to recognize Arabic Sign Language especially Tunisian accent, and subsequently translate it into spoken language utilizing artificial intelligence (AI) methods. The goal of this work is to facilitate communication between individuals who are deaf and those who are not. So, this paper presents the comparative results of four deep-learning models: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Reccurent Unit (GRU), and Bidirectional Gated Reccurent Unit (Bi-GRU), aiming to improve the recognition accuracy of dynamic signs. The results indicate that the BiLSTM outperforms other variants, showcasing an accuracy of 98%.

关键词： Sign language Accuracy Recurrent neural networks Visual analytics Logic gates Streaming media Artificial intelligence Information technology Long short term memory Streams

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FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning

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Procedia Computer Science 2024年 246卷 3644-3653页

作者： Grina Wiem Douik Ali National Engineering School of Monastir University of Monastir Rue Ibn Jazzar 5035 Monastir Tunisia Networked Objects Control and Communication Systems Laboratory Sousse Technology Centre National School of Engineers of Sousse University of Sousse Sahloul Belt Road 4054 Sousse Tunisia

关键词： Unsupervised Facial Pose Analysis Contrastive Learning Disentanglement Pose Estimation

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Hybrid approach for speaker recognition based on formant and pitch extraction 22

Hybrid approach for speaker recognition based on formant and...

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22nd International Conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar Tunisia National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This paper provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW)to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy. © 2023 IEEE.

关键词： Feature extraction

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Person Identification with Voice and Ear-print in Degraded Conditions for Smart Home Access 22

Person Identification with Voice and Ear-print in Degraded C...

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22nd International Conference on Cyberworlds, CW 2023

作者： Boujnah, Sana Ferjaoui, Radhia Khalifa, Anouar Ben University of Tunis El Manar National Engineering School of Tunis Tunisia NOCCS-Laboratory of Networked Objects Control and Communication Systems Eniso Tunisia Istmt Tunis Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9798350315653

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier. © 2023 IEEE.

关键词： Biometrics

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Analysis and optimisation of 8 × 1 double 'U' slotted patch array for future 5G communications

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International Journal of systems, control and communications 2020年第3期11卷 305-319页

作者： Zaidi, Abir Baghdad, Abdennaceur Awan, Wahaj Abbas Faleh, Souhir Ballouk, Abdelhakim Badri, Abdelmajid Mohammedia Laboratory EEA and TI Hassan II University Casablanca Morocco Department of Electrical and Computer Engineering COMSTAS University Islamabad Sahiwal Pakistan Networked Objects Control and Communication Systems Laboratory National Engineers School of Sousse University of Sousse Sousse Tunisia

In this manuscript, an 8 × 1 rectangular 'U' slotted patch antenna array designed for future 5G communications is presented. The proposed antenna is designed using the 3D EM CSTv18 microwave studio based on the finite difference time domain (FDTD) method, and compared using HFSSv19 EM (high frequency structure simulator) based on the finite element (FEM) method. Three scenarios are considered in this work, the first one is the analysis of antenna performance of single element, regular antenna array and slotted antenna array in order to evince the performance of the proposed antenna for future 5G communications. The designed array antenna presents good results in term of return loss, efficiency, VSWR, input impedance and gain. Moreover, a comparison between the proposed work and other related works is presented. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Millimeter waves

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