Statistical Assessment of the Performance of Dwt-Pca/Svd Recognition Algorithm on Reconstructed Frontal Face Images

dc.contributor.authorEssah, B.O.
dc.date.accessioned2021-10-29T10:11:32Z
dc.date.available2021-10-29T10:11:32Z
dc.date.issued2020-11
dc.descriptionMPhil. Statisticsen_US
dc.description.abstractFace recognition is the second most important biometric part of the human body, apart from the biometric nger print. Detecting and measuring half face image processing or pattern recognition is a challenge in this eld. The research made use of Discrete Wavelet Transform (DWT) as the preprocessing mechanism and adopted the Principal Component Analysis and Singular Value Decomposition (PCA/SVD) for feature extraction and recognition. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images are used for recognition. Statistical analysis using the Wilcoxon Sign Rank test shows that, there is no signi cant di erence in the left and right reconstructed half face images when DWT-PCA/SVD is used for recognition. In conclusion, DWT-PCA/SVD is therefore recommend as one of the best noise viable algorithm for recognizing face images under partial occlusion.en_US
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/36944
dc.language.isoenen_US
dc.publisherUniversity of Ghanaen_US
dc.subjectFace recognitionen_US
dc.subjectDiscrete Wavelet transformen_US
dc.subjectSingular Value Decompositionen_US
dc.subjectPrincipal Component analysisen_US
dc.titleStatistical Assessment of the Performance of Dwt-Pca/Svd Recognition Algorithm on Reconstructed Frontal Face Imagesen_US
dc.typeThesisen_US

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