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Super-resolution for biometrics: A comprehensive survey

Super-resolution for biometrics: A comprehensive survey
Super-resolution for biometrics: A comprehensive survey

The lack of resolution of imaging systems has critically adverse impacts on the recognition and performance of biometric systems, especially in the case of long range biometrics and surveillance such as face recognition at a distance, iris recognition and gait recognition. Super-resolution, as one of the core innovations in computer vision, has been an attractive but challenging solution to address this problem in both general imaging systems and biometric systems. However, a fundamental difference exists between conventional super-resolution motivations and those required for biometrics. The former aims to enhance the visual clarity of the scene while the latter, more significantly, aims to improve the recognition accuracy of classifiers by exploiting specific characteristics of the observed biometric traits. This paper comprehensively surveys the state-of-the-art super-resolution approaches proposed for four major biometric modalities: face (2D+3D), iris, fingerprint and gait. We approach the super-resolution problem in biometrics from several different perspectives, including from the spatial and frequency domains, single and multiple input images, learning-based and reconstruction-based approaches. Especially, we highlight two special categories: feature-domain super-resolution which performs super-resolution directly on the feature space to purposely improve the recognition performance, and deep-learning super-resolution which discusses the most recent advances in deep learning for the super-resolution task. Finally, we discuss the current and open research challenges and provide recommendations into the future for the improved use of super-resolution with biometrics.

Biometrics, Deep learning, Face recognition, Fingerprint recognition, Gait recognition, Human identification at a distance, Iris recognition, Non-ideal biometrics, Super-resolution
0031-3203
23-42
Nguyen, Kien
4aab1e76-def3-4419-bc3a-be4c404cfc94
Fookes, Clinton
d08f1a73-7438-41d4-8de2-053b2a5b011d
Sridharan, Sridha
6da7b54a-5714-4d62-8999-e46251801616
Tistarelli, Massimo
82800107-0ac6-4221-a972-6999ecc36152
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Nguyen, Kien
4aab1e76-def3-4419-bc3a-be4c404cfc94
Fookes, Clinton
d08f1a73-7438-41d4-8de2-053b2a5b011d
Sridharan, Sridha
6da7b54a-5714-4d62-8999-e46251801616
Tistarelli, Massimo
82800107-0ac6-4221-a972-6999ecc36152
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Nguyen, Kien, Fookes, Clinton, Sridharan, Sridha, Tistarelli, Massimo and Nixon, Mark (2018) Super-resolution for biometrics: A comprehensive survey. Pattern Recognition, 78, 23-42. (doi:10.1016/j.patcog.2018.01.002).

Record type: Article

Abstract

The lack of resolution of imaging systems has critically adverse impacts on the recognition and performance of biometric systems, especially in the case of long range biometrics and surveillance such as face recognition at a distance, iris recognition and gait recognition. Super-resolution, as one of the core innovations in computer vision, has been an attractive but challenging solution to address this problem in both general imaging systems and biometric systems. However, a fundamental difference exists between conventional super-resolution motivations and those required for biometrics. The former aims to enhance the visual clarity of the scene while the latter, more significantly, aims to improve the recognition accuracy of classifiers by exploiting specific characteristics of the observed biometric traits. This paper comprehensively surveys the state-of-the-art super-resolution approaches proposed for four major biometric modalities: face (2D+3D), iris, fingerprint and gait. We approach the super-resolution problem in biometrics from several different perspectives, including from the spatial and frequency domains, single and multiple input images, learning-based and reconstruction-based approaches. Especially, we highlight two special categories: feature-domain super-resolution which performs super-resolution directly on the feature space to purposely improve the recognition performance, and deep-learning super-resolution which discusses the most recent advances in deep learning for the super-resolution task. Finally, we discuss the current and open research challenges and provide recommendations into the future for the improved use of super-resolution with biometrics.

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More information

Accepted/In Press date: 7 January 2018
e-pub ahead of print date: 11 January 2018
Published date: 1 June 2018
Keywords: Biometrics, Deep learning, Face recognition, Fingerprint recognition, Gait recognition, Human identification at a distance, Iris recognition, Non-ideal biometrics, Super-resolution

Identifiers

Local EPrints ID: 419811
URI: https://eprints.soton.ac.uk/id/eprint/419811
ISSN: 0031-3203
PURE UUID: aee37739-d7c3-4b9e-986f-25f2efe851d7
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 20 Apr 2018 16:30
Last modified: 20 Apr 2018 16:30

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Contributors

Author: Kien Nguyen
Author: Clinton Fookes
Author: Sridha Sridharan
Author: Massimo Tistarelli
Author: Mark Nixon ORCID iD

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