Comparative analysis of selected facial recognition algorithms

Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined. Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition

Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot

Metaheuristics for solving facility location optimization problem

The increasing demand of optimization to various aspect of real-life complex combinatorial problems have seen a directly increase proportion of the use of metaheuristics as a technique many researchers applied to solve such problems. This is as a result of its flexibility, yet a complex approach that takes into consideration domain specific entities on how the problems are solve. One of such combinatorial problem applicable to real life issues is the facility location problem. Hence, this study sought to describe the concept of facility location problem, the techniques used to solve FLP (facility location problem) and real life application of FLP. Particle swarm optimization, genetic algorithm and tabu search are the most common metaheuristics used as solution tools to solving facility location problem from the review carried out. From the review, we recommended the application of more hybridized techniques to solve FLP in other to achieve better result.Keywords: Metaheuristics, Facility Location Problem (FLP), Optimizatio

Character recognition from image using radial basis function neural

Images contain different sorts of helpful data that ought to be removed at whatever point required and this data might be as content present in image. Character Recognition in Images has turned into a potential application in many fields like Image ordering, Robotics, Intelligent transport frameworks, etc. The main aim of this work is to create an optical character recognition software that extracts characters from image using Radial Basis Function Neural Networks (RBFNN) and develop an intelligent classifier system that recognizes and classifies text characters from images. The application is able to recognize characters online and offline. A test pad was created in the form of a drawing board, it converts the drawn character into an image to check if it can be recognized and the predicted character is displayed (online recognition). PredictCharacter.m is used for offline recognition; this function gives a list of possible characters with percentages and the one with the highest percentage is the answer. The acknowledgment rate of Radial Basis Function Neural Network (RBFNN) is observed to be best as the recognition rate in the proposed framework lies in the range of 95.5 and 97%.Keywords: Images, Character Recognition, Pattern Recognition, RBFN

Design and implementation of a biometric attendance system using facial recognition

This project is concerned with the development of a ‘Biometric attendance system using facial recognition’. The objective is to automate the traditional system of taking attendance in the department of Computer Science, University of Lagos. The problem with the traditional system of taking attendance is that there is no way of knowing when a student signs or indicates attendance for a colleague that is not present. This is where biometrics comes in. With the use of biometrics, attendance cannot be marked for any student that is not present for a class. The biometric software was developed using the fisher faces algorithm for facial recognition. Once a student has been registered on the system, the system will identify the student, and only then will it mark the student’s attendance based on its recognition of the students face. The software also calculates the percentage attendance of every student offering a course and this can be used to know student’s eligibility to take the examination for that course. The biometric attendance system will ensure accurate attendance records and can be used across different academic institutions.Keywords: Biometric, Fisher Faces Algorithm, Attendanc