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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences
Olabiyisi S. O.
Ladoke Akintola University of Technology
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Facial Image Verification and Quality Assessment System -FaceIVQA Omidiora E. O.; Olabiyisi S. O.; Ojo J. A.; Abayomi-Alli Adebayo; Abayomi-Alli O.; Erameh K. B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 6: December 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.358 KB)

Abstract

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.v3i6.5034
Development of software defect prediction system using artificial neural network Olatunji B. L.; Olabiyisi S. O.; Oyeleye C. A.; Sanusi B. A.; Olowoye A. O.; Ofem O. A.
International Journal of Advances in Applied Sciences Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (485.304 KB) | DOI: 10.11591/ijaas.v9.i4.pp284-293

Abstract

Software testing is an activity to enable a system is bug free during execution process. The software bug prediction is one of the most encouraging exercises of the testing phase of the software improvement life cycle. In any case, in this paper, a framework was created to anticipate the modules that deformity inclined in order to be utilized to all the more likely organize software quality affirmation exertion. Genetic Algorithm was used to extract relevant features from the acquired datasets to eliminate the possibility of overfitting and the relevant features were classified to defective or otherwise modules using the Artificial Neural Network. The system was executed in MATLAB (R2018a) Runtime environment utilizing a statistical toolkit and the performance of the system was assessed dependent on the accuracy, precision, recall, and the f-score to check the effectiveness of the system. In the finish of the led explores, the outcome indicated that ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE has the accuracy, precision, recall and the f-score of 86.93, 53.49, 79.31 and 63.89% respectively, 83.28, 31.91, 45.45 and 37.50% respectively, 83.43, 57.69, 45.45 and 50.84% respectively and 91.30, 33.33, 50.00 and 40.00% respectively. This paper presents an improved software predictive system for the software defect detections.
Co-Authors Abayomi-Alli Adebayo Abayomi-Alli O. Erameh K. B. Ofem O. A. Ojo J. A. Olatunji B. L. Olowoye A. O. Omidiora E. O. Oyeleye C. A. Sanusi B. A.