Reduced Set Kernel Principal Component Analysis (Rskpca) Algorithm for Palm Print Based Mobile Biometric System

Kemunculan baru dimensi internet dan teknologi tanpa wayar telah membawa era baru dalam teknologi biometrik. Selain sistem biometrik dengan peranti statik, sistem biometrik mudah alih boleh dilaksanakan dan pendekatan ini membawa kepada pelaksanaan yang lebih cekap dan efisien. Dalam kajian ini, sistem biometrik mudah alih berasaskan tapak tangan telah dibangunkan. Walau bagaimanapun, untuk melaksanakan sistem biometrik mudah alih, masa pemprosesan dan penyimpanan yang cekap adalah faktor penting yang perlu dipertimbangkan.Dalam kajian ini, beberapa algoritma yang melibatkan pemprosesan ciri tapak tangan dinilai berdasarkan penggunaan masa dan memori yang optimum. Beberapa kaedah pemprosesan ciri termasuk Ruang Dikehendaki (ROI), Analisa Komponen Utama (PCA) dan Analisa Komponen Utama Kernel (KPCA) disiasat. Pendekatan baru dalam pengekstrakan ciri yang digelar Analisa Komponen Utama Kernel Set Dikurangi (RSKPCA) dicadangkan untuk mempercepatkan pemprosesan pengekstrakan ciri. RSKPCA yang dicadangkan menggunakan anggaran Kepadatan set Dikurangkan (RSDE) untuk menentukan matriks gram yang wajar. Hasilnya, RSKPCA hanya mengekstrak maklumat yang paling relevan dan penting dari set data. 2400 imej tapak tangan yang telah dikumpul daripada tiga jenis peranti Android mudah alih. Penilaian eksperimen menunjukkan bahawa RSKPCA yang dicadangkan mempunyai prestasi lebih baik berbanding ROI, PCA dan KPCA dengan Kadar Penerimaan Tulen (GAR) adalah lebih daripada 98% dan masa pemadanan kurang daripada 0.5s. Projek ini telah membuktikan bahawa pengektsrakan ciri menggunakan RSKPCA yang dicadangkan memberikan keputusan yang terbaik untuk sistem biometrik mudah alih berasaskan imej tapak tangan. ________________________________________________________________________________________________________________________ The emerging of internet and wireless dimension has brought a new era in biometrics technology. Instead of operating the biometric system with static biometric device, mobile biometric system can be implemented and this approach leads to more efficient and reliable implementation. In this study mobile biometric system based on palm print modality is developed. However, in order to execute mobile biometric system, efficient processing time and storage are some of the important factors that need to be considered. In this research, algorithms involving palm print feature processing are evaluated so as to obtain optimum time and memory consumption. Several feature processing methods including Region of Interest (ROI), Principal Component Analysis (PCA), and Kernel Principal Component Analysis (KPCA) are investigated. A new approach in feature extraction called Reduced-Set Kernel Principal Component Analysis (RSKPCA) is proposed to speed up the processing in feature extraction. The proposed RSKPCA employs a Reduced Set Density Estimate (RSDE) to define a weighted gram matrix. As a result, the RSKPCA only extracts the most relevant and important information from a dataset. 2400 palm print images which were collected from three types of android mobile are employed. Experimental evaluation shows that the proposed RSKPCA has better performance compared to the ROI, PCA and KPCA with the Genuine Acceptance Rates (GAR) is more than 98% and the matching time is less than 0.5s. In this project, it has been proven that the proposed RSKPCA as feature extraction gives the best result for mobile biometric system based on palm print

A Fault-Tolerant Mobile Computing Model Based On Scalable Replica

The most frequent challenge faced by mobile user is stay connected with online data, while disconnected or poorly connected store the replica of critical data. Nomadic users require replication to store copies of critical data on their mobile machines. Existing replication services do not provide all classes of mobile users with the capabilities they require, which include: the ability for direct synchronization between any two replicas, support for large numbers of replicas, and detailed control over what files reside on their local (mobile) replica. Existing peer-to-peer solutions would enable direct communication, but suffers from dramatic scaling problems in the number of replicas, limiting the number of overall users and impacting performance. Roam is a replication system designed to satisfy the requirements of the mobile user. Roam is based on the Ward Model, replication architecture for mobile environments. Using the Ward Model and new distributed algorithms, Roam provides a scalable replication solution for the mobile user. We describe the motivation, design, and implementation of Roam and report its performance. Replication is extremely important in mobile environments because nomadic users require local copies of important data

PENGENALAN WAJAH SECARA REAL TIME DENGAN SMARTPHONE ANDROID

Manusia memiliki kemampuan untuk mengenali wajah dengan akurasi minimal 90% bahkan ketika tidak bertemu dengan wajah tersebut selama puluhan tahun, namun kemampuan seseorang untuk mengingat atau mencocokan wajah tersebut agak kurang. Terkadang pada saat menjumpai seseorang yang ada di album foto, seringkali merasakan sepertinya sudah pernah mengenali atau familiar dengan orang tersebut tetapi tidak mengingat bahkan lupa identitas siapakah orang tersebut. Oleh karena itu diperlukan suatu sistem untuk mengenali wajah seseorang yang dapat digunakan sebagai pengingat apakah seseorang yang dijumpai tersebut sudah pernah dikenali sebelumnya atau belum. Pada tulisan ini, penulis melakukan pengenalan wajah yang menggunakan metode eigenface sebagai metode yang digunakan secara real time. Pada pembuatan sistem ini, penulis menggunakan bahasa pemrograman java dan sistem operasi android sebagai platformnya. Tujuan dalam penulisan ini adalah dapat mengembangkan aplikasi pengenalan wajah berbasis mobile dengan tingkat akurasi dan kecepatan yang lebih baik secara real time. Pada tahap implementasi yang dilakukan, menghasilkan hasil pengenalan yang terbaik yaitu dengan dtingkat akurasi sebesar 66,67% dengan rata- rata waktu pengenalannya adalah 333,33 ms dengan kondisi pencahayaan yang mendukung

Open source face recognition API

Face recognition applications are widely used today for a variety of tasks, whether personal or professional. When looking for a service that provides face detection and classification, it is easy to find several solutions. In this project another way is described so that it is possible to perform this task according to the desired needs without the need to use proprietary software. With the emergence of the Django Rest Frame Work, web application development has become easier. This work describes development of stable foundation and features that offer an administration panel, relational database management, and support for a Restful Application Programming Interface (API). This takes advantage of the exclusive use of Open Source technologies thus the application code can be modified and distributed free of charge. For the development of an API that could perform detection and facial recognition, applying an Open Source philosophy, in addition to Django Rest Framework technologies such as Python, C++, MySql and JSON were used. The prototype is initially capable of recognizing the number of faces per image, assessing eyes, smile, age and gender. Flexibility is designed to increase application capabilities with new algorithms implemented in various programing languages.Atualmente, as aplicações de reconhecimento de facial são amplamente utilizadas para uma variedade de tarefas, pessoais ou profissionais. Ao procurarmos um serviço que forneça deteção e classificação de rosto, é fácil encontrar várias soluções. Neste projeto, é descrita outra maneira para que seja possível executar esta tarefa de acordo com as necessidades desejadas, sem a necessidade de usar software proprietário. Com o surgimento do Django Rest Framework, o desenvolvimento de aplicações web ficou mais fácil. Este trabalho descreve o desenvolvimento de bases e recursos estáveis que oferecem um painel de administração, gestão de uma base de dados relacional e o suporte para uma API (Application Programming Interface) Restful. Ao tirar proveito do uso exclusivo de tecnologias Open Source, é permitido que o código possa ser modificado e distribuído gratuitamente. Para o desenvolvimento de uma API que pudesse realizar a deteção e o reconhecimento facial, aplicando uma filosofia Open Source, para além da tecnologia Django Rest Framework foram utilizadas tecnologias como Python, C ++, MySql e JSON. O protótipo é inicialmente capaz de reconhecer o número de rostos por imagem, e avaliar olhos, sorriso, idade e sexo. Mas para além disso, foi projetada flexibilidade para aumentar os recursos através da implementação de novos algoritmos em várias linguagens de programação

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Selected Computing Research Papers Volume 7 June 2018

Contents Critical Evaluation of Arabic Sentimental Analysis and Their Accuracy on Microblogs (Maha Al-Sakran) Evaluating Current Research on Psychometric Factors Affecting Teachers in ICT Integration (Daniel Otieno Aoko) A Critical Analysis of Current Measures for Preventing Use of Fraudulent Resources in Cloud Computing (Grant Bulman) An Analytical Assessment of Modern Human Robot Interaction Systems (Dominic Button) Critical Evaluation of Current Power Management Methods Used in Mobile Devices (One Lekula) A Critical Evaluation of Current Face Recognition Systems Research Aimed at Improving Accuracy for Class Attendance (Gladys B. Mogotsi) Usability of E-commerce Website Based on Perceived Homepage Visual Aesthetics (Mercy Ochiel) An Overview Investigation of Reducing the Impact of DDOS Attacks on Cloud Computing within Organisations (Jabed Rahman) Critical Analysis of Online Verification Techniques in Internet Banking Transactions (Fredrick Tshane

MEC vs MCC: performance analysis of real-time applications

Hoje em dia, numerosas são as aplicações que apresentam um uso intensivo de recursos empurrando os requisitos computacionais e a demanda de energia dos dispositivos para além das suas capacidades. Atentando na arquitetura Mobile Cloud, que disponibiliza plataformas funcionais e aplicações emergentes (como Realidade Aumentada (AR), Realidade Virtual (VR), jogos online em tempo real, etc.), são evidentes estes desafios directamente relacionados com a latência, consumo de energia, e requisitos de privacidade. O Mobile Edge Computing (MEC) é uma tecnologia recente que aborda os obstáculos de desempenho enfrentados pela Mobile Cloud Computing (MCC), procurando solucioná-los O MEC aproxima as funcionalidades de computação e de armazenamento da periferia da rede. Neste trabalho descreve-se a arquitetura MEC assim como os principais tipos soluções para a sua implementação. Apresenta-se a arquitetura de referência da tecnologia cloudlet e uma comparação com o modelo de arquitetura ainda em desenvolvimento e padronização pelo ETSI. Um dos propósitos do MEC é permitir remover dos dispositivos tarefas intensivas das aplicações para melhorar a computação, a capacidade de resposta e a duração da bateria dos dispositivos móveis. O objetivo deste trabalho é estudar, comparar e avaliar o desempenho das arquiteturas MEC e MCC para o provisionamento de tarefas intensivas de aplicações com uso intenso de computação. Os cenários de teste foram configurados utilizando esse tipo de aplicações em ambas as implementações de MEC e MCC. Os resultados do teste deste estudo permitem constatar que o MEC apresenta melhor desempenho do que o MCC relativamente à latência e à qualidade de experiência do utilizador. Além disso, os resultados dos testes permitem quantificar o benefício efetivo tecnologia MEC.Numerous applications, such as Augmented Reality (AR), Virtual Reality (VR), real-time online gaming are resource-intensive applications and consequently, are pushing the computational requirements and energy demands of the mobile devices beyond their capabilities. Despite the fact that mobile cloud architecture has practical and functional platforms, these new emerging applications present several challenges regarding latency, energy consumption, context awareness, and privacy enhancement. Mobile Edge Computing (MEC) is a new resourceful and intermediary technology, that addresses the performance hurdles faced by Mobile Cloud Computing (MCC), and brings computing and storage closer to the network edge. This work introduces the MEC architecture and some of edge computing implementations. It presents the reference architecture of the cloudlet technology and provides a comparison with the architecture model that is under standardization by ETSI. MEC can offload intensive tasks from applications to enhance computation, responsiveness and battery life of the mobile devices. The objective of this work is to study and evaluate the performance of MEC and MCC architectures for provisioning offload intensive tasks from compute-intensive applications. Test scenarios were set up with use cases with this kind of applications for both MEC and MCC implementations. The test results of this study enable to support evidence that the MEC presents better performance than cloud computing regarding latency and user quality of experience. Moreover, the results of the tests enable to quantify the effective benefit of the MEC approach