DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING
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Item Quantitative evaluation of principal component analysis and fisher discriminant analysis techniques in face images.(Nigeria Computer Society, 2008) Omidiora, E. O.; Fakolujo, O. A.; Ayeni, R. O.; Olabiyisi, S. O.; Arulogun, O. T."Face recognition is an attractive field in enhancing both the security and the image retrieval activities in the multimedia world. Its natural basis in verification or identification purposes is a major factor of its wide acceptance in this evolving world of information technology. In this paper, experiments based on black African faces using Principal Component Analysis (OPCA) and Fisher Discriminant Analysis (OFDA) techniques were carried out. The design of the face recognition system was separated into three major sections - image acquisition and standardisation, dimensionality reduction, training and testing for recognition. Under static mode, experiments were performed on single scaled images without rotation, OPCA and OFDA both give recognition accuracies of between 89% and 97%;and) 88% and 98% respectively. These have been achieved at different levels of cropping. Despite the constraint created by the resources available, different results got showed that standard face recognition system could be developed using both algorithms. "Item Assessment of an access control system using principal component analysis and discrete cosine transform.(2008-08) Omidiora, E. O.||||Olabiyisi, S. O.; Fakolujo, O. A.; Olabiyisi, S. O.In this paper, faces were employed as the only control means of right of entrance and usage of information on the super-highway. Principal Component Analysis (PCA) and Discrete Cosine Transform (DCT) algorithms were employed as our basis of comparison. An assessment of both algorithms was considered, it was discovered that PCA proved to be a better algorithm for access control and recognition system because of its very high average percentage of rightly classified faces (90.43%) and its strict attendance to both FAR and FRR (0.1077, 0.0609) than DCT with 64.57% and: 0.24 and 0.02 respectively.Item A prototype of a robust and secured access control system using principal component analysis(2007) Omidiora, E. O.; Fakolujo, O. A.; Olabiyisi, S. O.; Ayeni, R. O.The need for a robust and secured access control system using a suitable algorithm is highly inevitable to forestall daily online harkers that are responsible in defrauding people of invaluable information and transactions worth billions of dollars in the process. In this paper, faces were employed as the only control means of right of entrance and usage of information on the super-highway. Principal Component Analysis (PCA) was used to perform dimensionality reduction on the feature vectors of the digitized face images. Also, Euclidean distance was the required similarity measure employed to match the tested face with the trained faces inside the database for actual recognition. The result obtained showed that its avarage percentage of rightly classified face was 90.43% and FAR and FRR were 0.1077 &0.0609. An evaluation of the results demostrated PCA to be a very good algorithm for a robust and secured access control and recognition system.