FACE RECOGNITION UNDER POOR ILLUMINATION USING HISTOGRAM EQUALIZATION AND ADAPTIVE SINGLE SCALE RETINEX

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dc.contributor.authorCemil T.
dc.date.accessioned2023-03-01T11:49:18Z
dc.date.available2023-03-01T11:49:18Z
dc.date.issued2014
dc.description.abstractThe Principal Component Analisys(PCA) is one of the most succesful techniques that have been used in face recognition in the images are sufficiently regular. But this algorithm has some deficiency in some cases such that pose variation, different facial expression or poor illumination. In this paper three different methods were compared and used to increase the recognition rates of them under varying illumination. After using PCA, the other methods (Histogram Equalization and Single scale retinex) were applied to images to have equally distributed illumination for all images. Eventually very high recognition rates were obtained after applying the methods by combining them.
dc.identifier.citationFACE RECOGNITION UNDER POOR ILLUMINATION USING HISTOGRAM EQUALIZATION AND ADAPTIVE SINGLE SCALE RETINEX / Cemil Turan / Suleyman Demirel University
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/121
dc.publisherSuleyman Demirel University
dc.subjectFace recognition
dc.subjectthe principal component method
dc.subjectalignment of the instogram
dc.subjectsingle-scale retinex
dc.titleFACE RECOGNITION UNDER POOR ILLUMINATION USING HISTOGRAM EQUALIZATION AND ADAPTIVE SINGLE SCALE RETINEX
dc.typeArticle
dspace.entity.type

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