Papers by Amusan Damilare
International Journal of Advanced Research in Computer Engineering & Technology International Journal of Advanced Research in Computer Engineering & Technology ISSN: 2278 – 1323 , 2019
Multimodal system is capable of increasing the scope and variety of input information the system ... more Multimodal system is capable of increasing the scope and variety of input information the system takes from the users for authentication. However, Face, Ear and fingerprint have compatibility formation but little research have been done in the area of comparing biometric features in order to determine their overall accuracy in multimodal systems. A total of 2160 datasets were used for this experiment. 1260 datasets were used for the training and 900 were used for testing. These images were preprocessed using histogram equalization and feature extraction was carried out using Principal component analysis (PCA). Self-organizing feature map (SOFM) and back propagation neural network (BPNN) was used for classification. The performance of the developed multimodal biometric systems (face, ear and finger) was compared and evaluated in MATLAB environment. The results showed that SOFM has high recognition accuracy and time than BPNN.
IJARCET, 2019
Multimodal system is capable of increasing the scope and variety of input information the system ... more Multimodal system is capable of increasing the scope and variety of input information the system takes from the users for authentication. However, Face, Ear and fingerprint have compatibility formation but little research have been done in the area of comparing biometric features in order to determine their overall accuracy in multimodal systems. A total of 2160 datasets were used for this experiment. 1260 datasets were used for the training and 900 were used for testing. These images were preprocessed using histogram equalization and feature extraction was carried out using Principal component analysis (PCA). Self-organizing feature map (SOFM) and back propagation neural network (BPNN) was used for classification. The performance of the developed multimodal biometric systems (face, ear and finger) was compared and evaluated in MATLAB environment. The results showed that SOFM has high recognition accuracy and time than BPNN.
In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) system using ar... more In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) system using artificial neural network is done. Vehicle License Plate Recognition (VLPR) is a special form of optical character recognition (OCR) which enables computer systems to read automatically the registration number of vehicles from digital pictures for the purpose of traffic control, security, access control to restricted areas, tracking of cars, tracing of stolen cars, identification of dangerous and reckless drivers on the road. This system is divided into three major parts: vehicle license plate detection, vehicle license plate character segmentation and License Plate character recognition. In vehicle license plate detection there are many challenges such as, complex plate background, illumination in consistencies, vehicle motion, distance changes for which edge detection analysis was explored. In this work, 200 vehicle license plates were captured, some with clear characters, others with blur and dirty stains. The character feature extraction and plate recognition accuracies were determined. Results showed that plates without blur and stain were most accurately extracted and recognized while satisfactory results were also obtained for the others.
The development of e-recruitment system is web-based tool used in order to reduce communication g... more The development of e-recruitment system is web-based tool used in order to reduce communication gap between job seekers and employers. E-recruitment, also known as online recruitment, is the practice of using technology and in particular Web-based resources for tasks involved with finding, attracting, assessing, interviewing and hiring new personnel. Most of the existing mode of recruitment (manual recruitment) takes much time in processing the application form, existing system will not automatically send feedback to all applicant whose meet up with the job requirement but with the help of the developed system there is reduction in time to process the application form and there is automatic feedback from the employer to the job seeker that meet up with requirement. The objective of this work is to developed an efficient e-recruitment system capable of managing all stages of the e-recruitment process, including multi-job posting, agency channel management and candidate filtering to identify the most relevant candidates. The development e-recruitment system employs 3-tier web architecture. The system consisted of design activities that produce system specifications satisfying the functional requirements that were developed in the system analysis process. A Unified Modeling Language (UML) was used to build a formal model of the university recruitment system. The Web-based University Recruitment System (WBURS) was designed to be user friendly and it is easy to navigate. A Macromedia dream wave was used in coding and developing website; SWISHmax was also used for creating graphics and animation in developing the website. Structured Query Language (SQL) was employed in creation of the database for the website and adoption of PHP (Hypertext Processor) was adopted to connect the website to a database. The performance of the developed system was evaluated by consulting three university staff and relevant information was collated through personal interviews and questionnaires was administered to the staff of those university, Human Resource Departments and other relevant professionals of these university.
Abstract: In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) syste... more Abstract: In this paper, a development of Nigeria Vehicle License Plate Recognition (NVLPR) system using artificial neural network is done. Vehicle License Plate Recognition (VLPR) is a special form of optical character recognition (OCR) which enables computer systems to read automatically the registration number of vehicles from digital pictures for the purpose of traffic control, security, access control to restricted areas, tracking of cars, tracing of stolen cars, identification of dangerous and reckless drivers on the road. This system is divided into three major parts: vehicle license plate detection, vehicle license plate character segmentation and License Plate character recognition. In vehicle license plate detection there are many challenges such as, complex plate background, illumination in consistencies, vehicle motion, distance changes for which edge detection analysis was explored. In this work, 200 vehicle license plates were captured, some with clear characters, others with blur and dirty stains. The character feature extraction and plate recognition accuracies were determined. Results showed that plates without blur and stain were most accurately extracted and recognized while satisfactory results were also obtained for the others.
Keywords: Software, License plate detection and recognition, Optical Character Recognition (OCR), Nigerian vehicle license plate, artificial neural network.
ABSTRACT: Vehicle License Plate Identification and Recognition (VLPIR) uses image processing and ... more ABSTRACT: Vehicle License Plate Identification and Recognition (VLPIR) uses image processing and character recognition technology in order to identify the license plate number automatically. VLPIR system are used for the purpose of effective traffic control, security applications such as access control to restricted areas and tracking of car, tracing of stolen cars, identification of dangerous and reckless drivers on the road. The objective of this work is to develop an algorithm for Vehicle License Plate identification and Recognition (VLPIR) of Nigeria License Plates. Standard Nigeria plate numbers consists of different colors such as the background colour (white), background image (Nigerian Map in green colour) and the number colour (red or blue). The system are divided into three, vehicle license plate extraction, character segmentation and character recognition, since the rows that contain the number plates are expected to exhibit many sharp variations. Hence, edge detection technique is used to find the location of the plate, vertical and horizontal projection is exploited to perform the character segmentation to ease and improve recognition rate. Median filter is used to remove noise, enhancement of the image to increase readability of the plate number. Neural network is used to recognize the vehicle license plate character.
KEYWORDS: Image processing, License plate localization and recognition, Plate numbers, neural network.
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Papers by Amusan Damilare
Keywords: Software, License plate detection and recognition, Optical Character Recognition (OCR), Nigerian vehicle license plate, artificial neural network.
KEYWORDS: Image processing, License plate localization and recognition, Plate numbers, neural network.
Keywords: Software, License plate detection and recognition, Optical Character Recognition (OCR), Nigerian vehicle license plate, artificial neural network.
KEYWORDS: Image processing, License plate localization and recognition, Plate numbers, neural network.