Abstract
The paper presents a novel feature extraction technique for face recognition which uses sparse projection axes to compute a lowdimensional representation of face images. The proposed technique derives the sparse axes by first recasting the problem of face recognition as a regression problem and then solving the new (under-determined) regression problem by computing the solution with minimum L1 norm. The developed technique, named Sparse Projection Analysis (SPA), is applied to color as well as grey-scale images from the XM2VTS database and compared to popular subspace projection techniques (with sparse and dense projection axes) from the literature. The results of the experimental assessment show that the proposed technique ensures promising results on un-occluded as well occluded images from the XM2VTS database.
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BibTeX (Download)
@conference{ICIAR2010_Sparse,
title = {Face recogniton from color images using sparse projection analysis},
author = {Vitomir \v{S}truc and Nikola Pave\v{s}i\'{c}},
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/ICIAR2010_1.pdf},
year = {2010},
date = {2010-06-01},
booktitle = {Proceedings of the 7th International Conference on Image Analysis and Recognition (ICIAR 2010)},
pages = {445-453},
address = {Povoa de Varzim, Portugal},
abstract = {The paper presents a novel feature extraction technique for face recognition which uses sparse projection axes to compute a lowdimensional representation of face images. The proposed technique derives the sparse axes by first recasting the problem of face recognition as a regression problem and then solving the new (under-determined) regression problem by computing the solution with minimum L1 norm. The developed technique, named Sparse Projection Analysis (SPA), is applied to color as well as grey-scale images from the XM2VTS database and compared to popular subspace projection techniques (with sparse and dense projection axes) from the literature. The results of the experimental assessment show that the proposed technique ensures promising results on un-occluded as well occluded images from the XM2VTS database.},
keywords = {biometrics, face verification, ICIAR, performance evaluation, sparse projection analysis},
pubstate = {published},
tppubtype = {conference}
}