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|Title:||Quantitative evaluation of principal component analysis and fisher discriminant analysis techniques in face images.|
|Authors:||Omidiora, E. O.|
Fakolujo, O. A.
Ayeni, R. O.
Olabiyisi, S. O.
Arulogun, O. T.
principal component analysis,
|Publisher:||Nigeria Computer Society|
|Abstract:||"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. "|
|Appears in Collections:||scholarly works|
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|(18)ui_art_omidiora_quantitative_2008.pdf||2.7 MB||Adobe PDF|
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