Abstract
Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. Recently, we presented Annotated Web Ears dataset that opens new challenges in ear recognition. In this paper we test the influence of alignment on recognition performance and prove that even with the alignment the database is still very challenging, even-though the recognition rate is improved due to alignment. We also prove that more sophisticated alignment methods are needed to address the AWE dataset efficiently
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@inproceedings{RibicERK2016,
title = {Influence of alignment on ear recognition : case study on AWE Dataset},
author = {Metod Ribi\v{c} and \v{Z}iga Emer\v{s}i\v{c} and Vitomir \v{S}truc and Peter Peer },
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/Influence_of_Alignment_on_Ear_Recognitio.pdf},
year = {2016},
date = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
pages = {131-134},
address = {Portoro\v{z}, Slovenia},
abstract = {Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest challenges of ear recognition: sensitivity to pose variations. Recently, we presented Annotated Web Ears dataset that opens new challenges in ear recognition. In this paper we test the influence of alignment on recognition performance and prove that even with the alignment the database is still very challenging, even-though the recognition rate is improved due to alignment. We also prove that more sophisticated alignment methods are needed to address the AWE dataset efficiently},
keywords = {AWE, AWE dataset, biometrics, ear alignment, ear recognition, image alignment, Ransac, SIFT},
pubstate = {published},
tppubtype = {inproceedings}
}