Sparse l2-norm Regularized Regression for Face Recognition

Ahmad Qudaimat, Hasan Demirel

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.

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Paper Citation


in Harvard Style

Qudaimat A. and Demirel H. (2019). Sparse l2-norm Regularized Regression for Face Recognition.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 453-458. DOI: 10.5220/0007355104530458


in Bibtex Style

@conference{icpram19,
author={Ahmad Qudaimat and Hasan Demirel},
title={Sparse l2-norm Regularized Regression for Face Recognition},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={453-458},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007355104530458},
isbn={978-989-758-351-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Sparse l2-norm Regularized Regression for Face Recognition
SN - 978-989-758-351-3
AU - Qudaimat A.
AU - Demirel H.
PY - 2019
SP - 453
EP - 458
DO - 10.5220/0007355104530458