Application of Computer Graphics Technique to Computer System Assembling

Computer graphics is the representation and manipulation of image data by a computer using various technology to create and manipulate images (Shirley et.al., 2005). The development of computer graphics has made computer easier to interact with, and better for understanding and interpreting different types of data. Three-Dimensional (3D) computer graphics represent geometric data that is stored in the computer for the purpose of performing calculations and rendering 2D images which may be for lateral display or for real-time viewing. In this work, 3D computer graphic software is used to produce a model of a real - life assembling of computer devices into a full-blown desktop computer. The work is presented in a video viewing format tat will facilitate independent coupling of systems through a ‘watch-and-fix’ paradigm. Keywords: 2D, 3D, IDE, Assembling, Photo-realistic, Data-visualization, Rasterization

Iris feature extraction: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system

Implementation of a Modified Counterpropagation Neural Network Model in Online Handwritten Character Recognition System

Artificial neural networks are one of the widely used automated techniques. Though they yield high accuracy, most of the neural networks are computationally heavy due to their iterative nature. Hence, there is a significant requirement for a neural classifier which is computationally efficient and highly accurate. To this effect, a modified Counter Propagation Neural Network (CPN) is employed in this work which proves to be faster than the conventional CPN. In the modified CPN model, there was no need of training parameters because it is not an iterative method like backpropagation architecture which took a long time for learning. This paper implemented a modified Counterpropagation neural network for recognition of online uppercase (A-Z), lowercase (a-z) English alphabets and digits (0-9). The system is tested for different handwritten character samples and better recognition accuracies of 65% to 96% were obtained compared to related work in literature.   Keywords: Artificial Neural Network, Counterpropagation Neural Network, Character Recognition, Feature Extraction

Database Management System for a Digitized Medical Image

Medical images are critical component of the healthcare system with great impact on the society’s welfare. Traditionally, medical images were stored on films in developing country, but the advances in modern imaging modalities made it possible to store them electronically. Thus, this paper gave and developed a novel framework for storing, retrieving and processing digitized medical images. Digital medical informatics and images are commonly used in hospitals today because  of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. This work provides vivid solution to the problem encountered and the difficulties associated with the challenges of large memory utilization attributing to storing patient’s medical image information conveniently. Keyword: Database Management System, Digitized Medical images, Memory utilizatio

Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System

Face recognition is considered to be one of the most reliable biometrics where security issues are of concerned. Feature extraction which is a functional block of a face recognition system becomes a critical problem when there is need to obtain the best feature with minimum classification error and low running time. Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these systems remain an open problem. Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component Analysis) for face recognition system. A database of 360 face images was created by obtaining facial images from LAUTECH Biometric Research Group consisting of six facial expressions of 60 persons. Images were preprocessed (gray scaling, cropping and histogram equalization) and the kernel variants were used to extract distinctive features and reduce the dimensionality of each of the images from 600x800 pixels to four smaller dimensions: 50x50, 100x100, 150x150 and 200x200 pixels. Euclidean Distance similarity measure was used for classification. The performance of the three kernel variants was evaluated for face recognition system using 180 images for training and 180 images for testing using the following metrics: Recognition Accuracy (RA) and Recognition Time (RT). Empirical result indicate that KLDA performs best for face recognition system with an average accuracy of 94.52%.  The larger image dimension also results in better recognition performance. We intend to experiment on other classifiers for face recognition system in our future work. Keywords— Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear

COMPARATIVE ANALYSIS OF TWO BIOMETRIC ACCESS CONTROL SYSTEMS

This paper compared two Biometric Access Control Systems (BACS). The BACSs employed iris pattern for their authentication. Fast Fourier Transform-driven Access Control System (FACS) uses global iris features while Haar Wavelet Transform-driven Access Control System (HACS) uses local iris features for its template generation. Principal Component Analysis (PCA) was employed to select principal components of the extracted features (local and global). Fuzzy clustering was used for classification and Euclidean Distance (ED) as the distance metric. Experimental result showed that it took more time to train the HACS than FACS because of its intrinsic location in the iris images. It was discovered that global features driven Access Control System (FACS) with EER being 7.78 outperformed the local features driven Access Control System (HACS) with EER of 8.05. Though the two systems satisfied the benchmark of 80% for Recognition Accuracy (RA) of Biometric Systems, FACS exhibited RA of 89.87% while HACS achieved a RA of 83.83% when tested on iris images captured with CMITECH DMX-10 Portable USB 5.0 M pixel CCD Iris Camera at automatic convenient eye distances. Performance of global and local features on other biometric recognition systems can be tested and a means of combining the two features for hybridization can also be sought

DEVELOPMENT OF A MODIFIED CLONAL SELECTION ALGORITHM FOR FEATURE LEVEL FUSION OF MULTIBIOMETRIC SYSTEMS

Feature level fusion is the combination of biometric information contained in the extracted features of biometric images. However, feature-balance maintenance and high computational complexity are one of the major problems encountered when fusion is done at feature level. Therefore, in this paper, a Modified Clonal Selection Algorithm (MCSA) which is characterized by feature-balance maintenance capability and low computational complexity was developed for feature level fusion of multibiometric systems.The standard Tournament Selection Method (TSM) was modified by performing tournaments among neighbours rather than by random selection to reduce the between-group selection pressure associated with the standard TSM. Clonal Selection  algorithm was formulated by incorporating the Modified Tournament Selection Method  (MTSM) into its selection phase. The modified algorithm could be employed for feature level fusion of multibiometric systems

Performance Evaluation of Feature Extraction Techniques in Multi-Layer Based Fingerprint Ethnicity Recognition System

This paper is set out to evaluate the performance of feature extraction techniques that can determine ethnicity of an individual using fingerprint biometric technique and deep learning approach. Hence, fingerprint images of one thousand and fifty-four (1054) persons of three different ethnic groups (Yoruba, Igbo and Middle-Belt) in Nigeria were captured. Kernel Principal Component Analysis (K-PCA) and Kernel Linear Discriminant Analysis (KLDA) were used independently for feature extraction while Convolutional Neural Network (CNN) was used for supervised learning of the features and classification. The results showed that out of sixty (60) individual fingerprints tested, eight (8) were classified as Yoruba, forty-eight (48) as Igbo and four (4) as Hausa. The Recognition Accuracy for K-PCA was 93.97% and KLDA was 97.26%. For Average Recognition time, K-PCA used 9.98seconds while KLDA used 10.02seconds. The memory space utilized by K-PCA was 94.57KB while KLDA utilized 52.17KB. T-Test paired sample statistics was carried out on the result obtained; the outcome presented reveal that KLDA outperformed the K-PCA technique in terms of Recognition Accuracy. The relationship between the average recognition time () and threshold value () was found to be polynomial of order four (4) with a high correlation coefficient for KPCA and polynomial of order three (3) with a high correlation coefficient for KLDA. In terms of computation time analysis, KLDA is computationally more expensive than KPCA by reason of processing speed