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Sparse l2-norm Regularized Regression for Face Recognition

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dc.contributor.author Qudaimat, Ahmad
dc.contributor.author Demirel, Hasan
dc.date.accessioned 2022-05-31T09:44:20Z
dc.date.accessioned 2022-06-01T09:54:30Z
dc.date.available 2022-05-31T09:44:20Z
dc.date.available 2022-06-01T09:54:30Z
dc.date.issued 2019-01-01
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/8535
dc.description.abstract In this paper, a new `2-norm regularized regression based face recognition method is proposed, with `0-norm constraint to ensure sparse projection. The proposed method aims to create a transformation matrix that transform the images to sparse vectors with positions of nonzero coefficients depending on the image class. The classification of a new image is a simple process that only depends on calculating the norm of vectors to decide the class of the image. The experimental results on benchmark face databases show that the new method is comparable and sometimes superior to alternative projection based methods published in the field of face recognition. en_US
dc.language.iso en en_US
dc.publisher 8th International Conference on Pattern Recognition Applications and Methods en_US
dc.subject Sparsifying transform, Face recognition, Dictionary learning, Transform Learning en_US
dc.title Sparse l2-norm Regularized Regression for Face Recognition en_US
dc.type Article en_US

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