Mohamed Atri

Bio: Mohamed Atri is an academic researcher from King Khalid University. The author has contributed to research in topics: Deep learning & Field-programmable gate array. The author has an hindex of 19, co-authored 160 publications receiving 1421 citations. Previous affiliations of Mohamed Atri include University of Burgundy & University of Monastir.

Face Recognition Systems: A Survey

Abstract: Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

Definition and Performance Evaluation of a Robust SVM Based Fall Detection Solution

Abstract: We propose an automatic approach to detect falls in home environment. A Support Vector Machine based classifier is fed by a set of selected features extracted from human body silhouette tracking. The classifier is followed by filtering operations taking into account the temporal nature of a video. The features are based on height and width of human body bounding box, the user's trajectory with her/his orientation, Projection Histograms and moments of order 0, 1 and 2. We study several combinations of usual transformations of the features (Fourier Transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using a single camera.We evaluated the robustness of our method using a realistic dataset. Experiments show that the best tradeoff between classification performance and time processing result is obtained combining the original data with their first derivative. The global error rate is lower than 1%, and the recall, specificity and precision are high (respectively 0.98, 0.996 and 0.942). The resulting system can therefore be used in a real environment. Hence, we also evaluated the robustness of our system regarding location changes. We proposed a realistic and pragmatic protocol which enables performance to be improved by updating the training in the current location, with normal activities records.

A Survey of Localization Systems in Internet of Things

Abstract: With the rapid development in wireless technologies and the Internet, the Internet of Things (IoT) is envisioned to be an integral part of our daily lives. Localization-based services are among the most attractive applications related to the IoT. They are actually, thanks to the deployment of networks of sensors, able to collect and transmit data in order to determine the targets position. A plethora of localization systems are proposed in the literature. These localization systems are based on different positioning approaches, different techniques and different technologies, making them appropriate for some applications and inappropriate for other applications. This survey provides a general overview of the localization in Wireless Sensor Networks (WSN) and surveys technical details related to approaches and algorithms of various important localization techniques using different technologies. Based on the localization approaches, we propose to classify the localization systems to centralized, distributed and interactive. Considering the techniques of localization, we classify them to distance measurement, angle measurement, arear measurement and hop-count measurement based. Finally, Depending on the manner of the wireless devices interaction with the target, we classify the localization systems to two categories: device-based and device-free systems. In device-based techniques, localization is linked to the target, and localization is determined thanks to the cooperation with other deployed wireless devices. Whereas in the device-free systems, the target does not include any wireless device according to the localization. We compare exhaustively each system in terms of precision, cost, evolution and energy efficiency. Furthermore, we show the importance of localization in modern IoT application such as smart city, smart transportation and mobility. In this concern, we provide an overview of the main challenges of localization in IoT exposed recently in the literature. Finally, we suggest in this paper some future directions in localization studies. This paper intends to help new researchers in the field of localization and IoT by providing a comprehensive survey on recent advances in this field.

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

Abstract: We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user’s trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context

Abstract: This paper is about generalized Fourier descriptors, and their application to the research of invariants under group actions. A general methodology is developed, crucially related to Pontryagin's, Tannaka's, Chu's and Tatsuuma's dualities, from abstract harmonic analysis. Application to motion groups provides a general methodology for pattern recognition. This methodology generalizes the classical basic method of Fourier-invariants of contours of objects. In the paper, we use the results of this theory, inside a Support-Vector-Machine context, for 3D objects-recognition. As usual in practice, we classify 3D objects starting from 2D information. However our method is rather general and could be applied directly to 3D data, in other contexts. Our applications and comparisons with other methods are about human-face recognition, but also we provide tests and comparisons based upon standard data-bases such as the COIL data-base. Our methodology looks extremely efficient, and effective computations are rather simple and low cost. The paper is divided in two parts: first, the part relative to applications and computations, in a SVM environment. The second part is devoted to the development of the general theory of generalized Fourier-descriptors, with several new results, about their completeness in particular. These results lead to simple formulas for motion-invariants of images, that are "complete" in a certain sense, and that are used in the first part of the paper. The computation of these invariants requires only standard FFT estimations, and one dimensional integration.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A review on genetic algorithm: past, present, and future

Abstract: In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

An affine invariant salient region detector

Abstract: In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.