Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

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

DEVELOPMENT OF AN IMPACT ASSESSMENT ALGORITHM FOR THE ADOPTION OF INFORMATION AND COMMUNICATION TECHNOLOGY IN BASIC EDUCATION USING CROSS-IMPACT METHOD

In many countries, the adoption of Information and Communication Technology (ICT) in basic education has been continuously linked to higher efficiency, productivity, and educational outcomes, including quality of cognitive, creative and innovative thinking. This paper focuses on the development of an impact assessment algorithm for evaluating the adoption of ICT in basic education using Cross-impact method. A questionnaire on adoption of ICT in basic education was designed based on Government Policy (GP), Teacher Competency (TC), Availability of ICT infrastructure (IF), Integration of ICT in school curriculum by Ministry of Education (MC), Student preparedness in adopting ICT in learning process (SC) and Perception of schools’ management in adoption of ICT in schools (MI), which are the six major events considered. The questionnaire was administered to experts in basic education within the selected South-Western states of Nigeria (Oyo, Lagos and Ekiti). Experts’ opinions from the administered questionnaires were quantitatively analysed using descriptive statistic in Statistical Package for Social Sciences. The results obtained from the analysis of questionnaires were used to derive the Initial Probability (InitProb) and generate the Conditional Probability Matrices (CondProbMatrices) for occurrence and non-occurrence of the six events under consideration. The impact assessment algorithm was developed such that its starting instructions would determine the consistence of the InitProb and the CondProbMatrices using the three fundamental laws of probability calculus (Normalization, Product and Addition rules). These are followed by sequential instructions which would determine the occurrence of each event in the CondProbMatrices. Then, through repetitive instructions, each event would be selected at random and its occurrence and non-occurrence would be determined using a random number generator. The last group of instructions would successively determine the impact of each event on other alternative events. Thus, the developed impact assessment algorithm could replace the existing user perspective method of evaluating the adoption of ICT in basic education

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

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING OF DIGITAL MAMMOGRAPHIC IMAGES

Digital mammogram has become the most efficient tool for early breast cancer detection modalities and pre-processing these images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification.  Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for pre-processing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise

A Mathematical Programming Model and Enhanced Simulated Annealing Algorithm for the School Timetabling Problem

Despite significant research efforts for School Timetabling Problem (STP) and other timetabling problems, an effective solution approach (model and algorithm) which provides boundless use and high quality solution has not been developed. Hence, this paper presents a novel solution approach for solving school timetabling problem which characterizes the problem-setting in the timetabling problem of the high school system in Nigeria. We developed a mixed integer linear programming model and meta-heuristic method - Enhanced Simulated Annealing (ESA) algorithm. Our method incorporates specific features of Simulated Annealing (SA) and Genetic Algorithms (GA) in order to solve the school timetabling problem. Both our solution approach and SA approach were implemented using Matrix Laboratory 8.6 software. In order to validate and demonstrate the performance of the developed solution approach, it was tested with the highly constrained school timetabling datasets provided by a Nigerian high school using constraints violation, simulation time and solution cost as evaluation metrics. Our developed solution approach is able to find optimal solution as it satisfied all the specified hard and soft constraints with average simulation time of 37.91 and 42.16 seconds and solution cost of 17.03 and 18.99, respectively, for JSS and SSS to the problem instance. A comparison with results obtained with SA approach shows that the developed solution approach produced optimal solution in smaller simulation time and solution cost, and has a great potential to solve school timetabling problems with satisfactory results. The developed ESA algorithm can be used for solving other related optimization problems