Recognition of Face Images under Angular Constraints Using DWT-PCA/SVD Algorithm

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dc.contributor.authorAsiedu, L.
dc.contributor.authorMettle, F.O.
dc.contributor.authorNortey, E.N.N.
dc.contributor.authorYeboah, E.S.
dc.date.accessioned2019-07-26T15:53:00Z
dc.date.available2019-07-26T15:53:00Z
dc.date.issued2017-12
dc.description.abstractThe intricacy of a face’s features originates from continuous changes in the facial features that take place over time. Regardless of these changes, we are able to recognize a person very easily. In human interactions, the articulation and perception of constraints; like head-poses, facial expressions form a communication channel that is additional to voice and that carries crucial information about mental, emotional and even physical states of a conversation. Automatic face recognition is worthwhile, since an efficient and resilient recognition system is useful in many application areas. This paper presents an evaluation of the performance of principal component analysis with singular value decomposition using discrete wavelet transform (DWT-PCA/SVD) for preprocessing under angular constraints. Ten individuals from Massachusetts Institute of Technology (MIT) database (2003-2005) captured under the specified angular constraints were considered for recognition runs. Friedman’s rank sum test was used to ascertain whether significant differences exist between the median recognition distances of the various constraints from their straight-pose. Recognition rate and runtime were adopted as the numerical evaluation methods to assess the performance of the study algorithm. All numerical and statistical computations were done using Matlab. The results of the Friedman’s rank sum test show that the higher the degrees of head-pose, the larger the recognition distances and that at and above, the recognition distances become profoundly larger compared to the head-pose. The numerical evaluations show that DWT-PCA/SVD face recognition algorithm has an appreciable average recognition rate (87.5%) when used to recognize face images under angular constraints. Also, the recognition rate decreases for head-poses greater than Discrete wavelet transform is recommended as a viable noise removal mechanism that should be adopted during image preprocessing.en_US
dc.identifier.otherDOI: 10.17654/MS102112809
dc.identifier.otherVol.102(11)
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/31835
dc.language.isoenen_US
dc.publisherFar East Journal of Mathematical Sciencesen_US
dc.subjectFace Imagesen_US
dc.subjectDWT-PCA/SVD Algorithmen_US
dc.titleRecognition of Face Images under Angular Constraints Using DWT-PCA/SVD Algorithmen_US
dc.typeArticleen_US

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