The purpose of this application is to develop an embedded system application which is able to collect face image and recognize the face by comparing with the database inside the system. Face recognition as a type of biometric methods has the features of non-contact, safety and convenience. It is widely used in human-computer interaction, transaction authentication, security and other fields. Recent years, with the development of mobile internet and embedded computer, it becomes possible to run face recognition on embedded system. This type of application has huge potential in remote payment and personal information security. This application is running on Android operating system which is an operating system based on the Linux kernel, and designed primarily for touchscreen mobile devices such as smartphones and tablet computers. The procedure of face recognition includes face detection, face normalization and recognition. This paper studies these key issues and successfully developed an application with nice recognition rate. The main contents and results are as follows:1) Discusses the face detection method. It used Adaboost algorithm and Haar features to detect human faces.2) Studies image pre-processing methods. Standardize the images so as to minimize the storage space and speed up the computation speed. 3) Summarize a variety of face recognition algorithms especially principle component analysis which is used in this application. Discuss the theoretical foundation of PCA algorithm. 4) Fulfilled all the features from face detection to recognition in Android platform. Using ORL face image database for testing and got a correct identification rate of over 85%. Fully verify the effectiveness of the program. Discuss the results and identification strategies.
Human Face is the most visible part which can be used to recognize persons. There are many available systems for face recognition in the market, but they are bulky and expensive. The implementation of face recognition techniques in an embedded system is a very important aspect. This project involves design of a real-time, portable, low embedded cost face recognition system. Implementation and analysis of face recognition techniques on an embedded system, the development phase consists of Single Board Computer (SBC, Raspberry Pi (Model A) as process unite, and GNU/Linux based Embedded Raspbian Operating system is used as application development platform. This project focuses to apply the face recognition algorithm that is suitable with Raspberry Pi (Model A) The proposed system is implemented using ARM11 processor and inefficient memory on Raspberry Pi (Model A) board, to get an acceptable performance of the system, the images are captured at resolution (320×240), the system needs ≈ 2.1 sec to process the captured images, The performance of the embedded system is done by evaluating detection time and recognition time (is 1.75 sec, between 0.29 sec to 0.74 sec) respectively, together with CPU utilization and RAM utilization (33%, 17.75%) for detection and (36.5%, 22%) for recognition. Results obtained shows that the overall performance on the embedded system can be increased when motion detection techniques is applied.
This book covers a wide range of challenges, technologies and state-of-the-art for the design, development and realization of smart and complex embedded systems and their applications; i.e., software and hardware development, with the use of digital technologies, and quality assurance for critical applications. This book starts with automotive safety systems which is one of the major functional domains. It discusses the importance of software in automotive systems followed by an insight into Automotive Software Standards, ISO26262, and Autosar. The book further discusses the use of Processor in the loop test for an adaptive trajectory tracking control for quadrotor UAVs. It also illustrates the role of embedded systems in medical engineering. Various innovative applications involving the concept of image processing and Internet of Things are also presented in this book. The SoC Power Estimation is also investigated. Finally, a Review of the Hardware/Software Partitioning Algorithms with some future works have been presented. this book is intended for academicians, researchers, and industrialists.
In the past half century growth of computer systems has made mankind's life much easier than before. This growth in technology of computer systems improved our lifestyle in different criteria and developed our knowledge even faster than before. Quality management of products in mega factories to micro computers which are used in medical technologies to make surgery process faster and more accurate are just a few instances of this improvements. Embedded system is one of the usages of this new innovated technology for the past decades. An embedded system is a system that has everything which a computer needs to run, like processor and memory and IO (Input and Output), but in a very small scale. Though, the point of Embedded system is that it can run in unstable conditions; for example, in hot weather or under water or places that have enormous shakes or etc. On the other hand, one of the areas which has been very popular in computer field is photography and image processing. Different types of computers can be used, based on our requirements, to do jobs like noise reduction, pattern recognition, obtaining 3D pictures and in lots of other fascinating domains. Face detection, a kind of pattern detection, is a pre-requirement of face recognition that has enormous usage these days. This field has also been considered as one of the most popular research areas since annually more than a thousand of titles published in main engineering and scientific journals are allocated to this area, academic databases journals such as "Academic Search Premiers", "IEEE Explore", "SCOPUS", and other important databases contain several research works in the field of face detection. The two main important issues in face detection processing are time consumption optimization with the intention of reaching highest speed possible while maintaining the second aspect which is an acceptable accuracy of the detection results. Thus, C and/or C++ are employed to do the detection process as fast as possible mainly because they are of good programming languages that are considered close to low level languages, in spite the fact that they are high level programming languages. Other high level programming languages have not been used in this area due to the fact that they have more latency for execution compared to these two languages. One of these popular high level programming languages is Java. Java is an object oriented programming language and it has lots of open source libraries that implements different classes for different purposes, which can be used by programmers. But the main thing that has made Java programming language the most popular one for software developers is that it is platform independent. For languages like C or C++ the source code should be compiled for each platform separately; consequently, the outputs of programs are not guaranteed to be the same for them as well. However, as programs written in Java run on a virtual machine and this virtual machine itself runs on top of operating systems, outputs of the programs are guaranteed to be the same for all of these operating systems. Actually, the only thing that should be provided is an interpreter which is the JVM (Java Virtual Machine) installed on top of the OS (Operating System). According to Barr & Frank [1], this virtual machine as an interpreter causes at least ten percent of latency to provide outputs for a program which is written in java compared to the same program that is written in C and/or C++. From embedded system point of view, it is also an issue as there is no software for JVM provided in this area. Fortunately, few processors have been developed recently which are able to execute java programs directly on the processor with no need of an interpreter. These processors implement JVM in hardware, which are mostly soft-cores, regards to research purpose, and sometimes hardcore architecture instead of a JVM application. As a result, it is expected that the mentioned extra latency which is caused by virtual machine interpreter is avoided for an embedded system that runs java programs through usage of hardware implemented JVMs. The aim of this research is to run a face detection application which is implemented by java on a hardware implementation of Java Virtual Machine (JVM) in an embedded system and compare it with the PC (Personal Computer) version of the same program. Moreover, problems that might occur in whole process will be inspected and analyzed. Finally, the result will be compared with a couple of other face detectors which was previously proposed by Sim and Yan as their Master of Engineering Thesis. First chapter gives a brief introduction towards the whole idea of research. A quick history of face detection systems is provided in this chapter and the outline of the thesis is explained as well. The second chapter brings an insight over current algorithms of face detection and it is followed by an explanation on the algorithm which has been used in this research in full details. Then in third chapter, we will talk about Java programming language and Java Virtual Machine and its implementations, especially hardware implementation. After all in this chapter, JOP (Java Optimized Processor) will be reviewed as a hardware implementation of JVM, the manner by which it has been used in this research. The forth chapter explains the face detection application which is implemented in Java by one of the open source libraries. In the fifth chapter the very same program will be mapped to the JOP and implemented by DE2-115 FPGA board. Eventually, I will bring future works and possible improvement for the research in the last chapter.
These are the proceedings of the International Conference on ISMAC-CVB, held in Palladam, India, in May 2018. The book focuses on research to design new analysis paradigms and computational solutions for quantification of information provided by object recognition, scene understanding of computer vision and different algorithms like convolutional neural networks to allow computers to recognize and detect objects in images with unprecedented accuracy and to even understand the relationships between them. The proceedings treat the convergence of ISMAC in Computational Vision and Bioengineering technology and includes ideas and techniques like 3D sensing, human visual perception, scene understanding, human motion detection and analysis, visualization and graphical data presentation and a very wide range of sensor modalities in terms of surveillance, wearable applications, home automation etc. ISMAC-CVB is a forum for leading academic scientists, researchers and research scholars to exchange and share their experiences and research results about all aspects of computational vision and bioengineering.
This book constitutes the refereed proceedings of the Third International Conference on Embedded Software and Systems, ICESS 2007, held in Daegu, Korea, May 2007. The 75 revised full papers cover embedded architecture, embedded hardware, embedded software, HW-SW co-design and SoC, multimedia and HCI, pervasive/ubiquitous computing and sensor network, power-aware computing, real-time systems, security and dependability, and wireless communication.
The International Conference on Intelligent Computing (ICIC) was formed to p- vide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and ind- try to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. ICIC 2008, held in Shanghai, China, September 15–18, 2008, constituted the 4th International Conference on Intelligent Computing. It built upon the success of ICIC 2007, ICIC 2006 and ICIC 2005 held in Qingdao, Kunming and Hefei, China, 2007, 2006 and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Emerging Intelligent Computing Technology and Applications”. Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
The nature of handwriting in our society has significantly altered over the ages due to the introduction of new technologies such as computers and the World Wide Web. With increases in the amount of signature verification needs, state of the art internet and paper-based automated recognition methods are necessary. Pattern Recognition Technologies and Applications: Recent Advances provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification.
Abstract: The current project presents the development of R-Eye, a face detection and tracking system implemented as an embedded device based on the Arduino microcontroller. The system is programmed in Python using the Viola-Jones algorithm for image processing. Several experiments designed to measure and compare the performance of the system under various conditions show that the system performs well when used with an integrated camera, reaching a 93% face recognition accuracy for a clear face. The accuracy is lower when detecting a face with accessories, such as a pair of eyeglasses (80%), or when a low-resolution low-quality camera is used. Experimental results also show that the system is capable of detecting and tracking a face within a frame containing multiple faces.
