Face Recognition Using Curvelet and Waveatom Transform

The field of digital image processing is continually evolving. Nowadays, there is a significant increase in the level of interest in image morphology, neural networks, full-color image processing, image data compression and image recognition. This work deals with image recognition with the application of face recognition. Some people think that face recognition is an easy task for computer system as for humans, but in reality most of the face recognition systems can’t achieve a complete reliable performance because there are many factors affect on the process of recognition like: large variations in facial approach, head size and orientation, and change in environmental conditions, all these factors makes face recognition one of the fundamental problems in pattern analysis, other factors that impact the performance are the accuracy of face location stage and the number of actual face recognition techniques used in each system. So face recognition from still and video images is emerging as an active research area with numerous commercial and law enforcement application. This research identifies two techniques for face features extraction based on two different multiresolution analysis tools; the first called Curvelet transform while the second is waveatom transform. The resultant features are inputted to train via two famous classifiers; one of them is the artificial neural network (ANN) and the other is hidden Markov model (HMM). Experiments are carried out on two well-known datasets; AT&T dataset consists of 400 images corresponding to 40 people, and Essex Grimace dataset consists of 360 images corresponding to 18 people. Experimental results show the strength of both curvelets and waveatom features. On one hand, waveatom features obtained the highest accuracy rate of 99% and 100% with HMM classifier, and 98% and 100% with ANN classifier, for AT&T and Essex Grimace datasets, respectively. On the other hand, two levels Curvelet features achieved accuracy rate of 98% and 100% with HMM classifier, and 97% and 100% with ANN classifier, for AT&T and Essex Grimace datasets, respectively. A comparative study for waveatom with wavelet-based, curvelet-based, and traditional Principal Component Analysis (PCA) techniques is also presented. The proposed techniques supersede all of them. And shows the robustness of feature extraction methods used against included and occluded effects. Also, indicates the potential of HMM over ANN, as they are classifiers

Survey of Platforms for Massive IoT

Internet of things (IoT) becomes a prominent technology in our world. It is enabling the connection between the objects (the “things”) and the backend systems via the Internet. Everyday objects can become connected and smart. It has been adopted in different areas and applications such as smart cities, smart agriculture, smart healthcare, smart manufacturing, and others. Moreover, IoT platforms are currently growing up into the market. Each platform provides valuable and specific services and features. This paper presents a survey on IoT platforms, discussing their architectures and fundamentals of IoT building elements and communication protocols between them. The aim of this paper is to help the reader choose a suitable and adequate IoT platform for own demands in the huge number and variety of platforms available. This survey provides a comprehensive view of the components and features of the state-of-the-art IoT platforms

Evaluation of IoT Device Management Tools

Industry 4.0 with IoT (Internet of Things) is the next wave in technology revolution which is expected to change our everyday life. This digitalization is having great impact on all the domains (energy, healthcare, transportation, manufacturing etc.) in addition to the ICT (Information and Communication Technologies) sector. In IoT scenarios, numerous sensors measure and report several phenomena and diversified IoT solutions are deployed to collect huge amount of data. IoT platforms, such as Amazon AWS, IBM Watson or Microsoft IoT Suite, have been available to aid the development of such services/applications. However, one of the major challenges faced by IoT solutions providers is the supervision and management of the large number of deployed sensors/devices. Presumably, the magnitude and heterogeneity of the IoT systems makes it difficult to manage them with conventional IT management tools and techniques. New techniques and tools have to be explored and developed or the traditional management solutions have to be adapted to the new challenges. In this paper, we identify and formulate the essential challenges of IoT device management and supervision, review the actual state-of-the-art IoT device management and supervision techniques and tools available on the market, and briefly evaluate their features and typical use cases