research-article

Multimodal hybrid features in 3D ear recognition

Authors:

Karthika Ganesan,

Chilambuchelvan A,

Iyyakutti Iyappan Ganapathi,

Naoufel WerghiAuthors Info & Claims

Applied Intelligence, Volume 53, Issue 10

Pages 11618 - 11635

https://doi.org/10.1007/s10489-022-04071-5

Published: 08 September 2022 Publication History

Abstract

Despite being the most rapidly evolving biometric trait, the ear suffers from a few drawbacks, such as being affected by posture, illumination, and scaling in the two-dimensional domain. To address these issues, researchers focused on the 3D domain, as the intrinsic features of the 3D ear have significantly contributed to performance enhancement. However, it has also been observed that combining 2D and 3D ear features improves recognition better than using 3D ear features alone. This article presents a hybrid descriptor where the feature vectors for ear recognition are derived from the multimodal ear using classical and learning-based approaches. The classical approach generates features using a covariance matrix, whereas the learning-based approach uses a deep auto-encoder to generate features. Further, these features are combined to create a hybrid descriptor. Thorough experiments were carried out on the largest publicly accessible ear database to demonstrate the effectiveness of the proposed approach and compare its performance to that of state-of-the-art techniques.

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Cited By

Mehta RUjjwal GKumar A(2024)An Efficient 3D Ear Recognition Method Using Point Set Registration ApproachSN Computer Science10.1007/s42979-024-03078-85:6Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1007/s42979-024-03078-8
Li DLiang HLam K(2024)AFSRNet: learning local descriptors with adaptive multi-scale feature fusion and symmetric regularizationApplied Intelligence10.1007/s10489-024-05418-w54:7(5406-5416)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05418-w

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Information

Published In

cover image Applied Intelligence

Applied Intelligence Volume 53, Issue 10

May 2023

1650 pages

ISSN:0924-669X

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 08 September 2022

Accepted: 08 August 2022

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Citations

Cited By

Mehta RUjjwal GKumar A(2024)An Efficient 3D Ear Recognition Method Using Point Set Registration ApproachSN Computer Science10.1007/s42979-024-03078-85:6Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1007/s42979-024-03078-8
Li DLiang HLam K(2024)AFSRNet: learning local descriptors with adaptive multi-scale feature fusion and symmetric regularizationApplied Intelligence10.1007/s10489-024-05418-w54:7(5406-5416)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05418-w

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