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On Learning Joint Multi-biometric Representations by Deep Fusion

Damer, Naser and Dimitrov, Kristiyan and Braun, Andreas and Kuijper, Arjan (2019):
On Learning Joint Multi-biometric Representations by Deep Fusion.
pp. 1-8, IEEE, 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019), Tampa, USA, 23.-26.09., ISBN 978-1-7281-1523-8,
DOI: 10.1109/BTAS46853.2019.9186011,
[Conference or Workshop Item]

Abstract

Multi-biometrics combines different biometric sources to enhance recognition, template protection, and indexing performances. One of the main challenges here is the need for joint discriminant feature representation of multi-biometric data. This is typically achieved by feature-level fusion, imposing limitations on the combinations of biometric characteristics and algorithms. Including multiple imaging sources within deep-learning networks was generally limited to multiple sources of images of the same physical object, e.g., multi-spectral object detection. Previous biometrics works were limited to use deep-learning to extract representations of single biometric characteristics. In contrast to that, our work studies creating representations of one identity by sampling different physical objects, i.e. biometric characteristics. We adapted three architectures successfully to produce and discuss jointly learned representations for different levels of correlated data, modalities, instances, and presentations. Our evaluation proved the applicability of jointly learning biometric representations, especially when the data correlation is low.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Damer, Naser and Dimitrov, Kristiyan and Braun, Andreas and Kuijper, Arjan
Title: On Learning Joint Multi-biometric Representations by Deep Fusion
Language: English
Abstract:

Multi-biometrics combines different biometric sources to enhance recognition, template protection, and indexing performances. One of the main challenges here is the need for joint discriminant feature representation of multi-biometric data. This is typically achieved by feature-level fusion, imposing limitations on the combinations of biometric characteristics and algorithms. Including multiple imaging sources within deep-learning networks was generally limited to multiple sources of images of the same physical object, e.g., multi-spectral object detection. Previous biometrics works were limited to use deep-learning to extract representations of single biometric characteristics. In contrast to that, our work studies creating representations of one identity by sampling different physical objects, i.e. biometric characteristics. We adapted three architectures successfully to produce and discuss jointly learned representations for different levels of correlated data, modalities, instances, and presentations. Our evaluation proved the applicability of jointly learning biometric representations, especially when the data correlation is low.

Publisher: IEEE
ISBN: 978-1-7281-1523-8
Uncontrolled Keywords: Biometrics, Multibiometrics, Information fusion, Face recognition, Iris recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 10th International Conference on Biometrics Theory, Applications and Systems (BTAS 2019)
Event Location: Tampa, USA
Event Dates: 23.-26.09.
Date Deposited: 22 Sep 2020 13:27
DOI: 10.1109/BTAS46853.2019.9186011
Official URL: https://doi.org/10.1109/BTAS46853.2019.9186011
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