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Supervised generalized canonical correlation analysis of soft biometric fusion for recognition at a distance

Supervised generalized canonical correlation analysis of soft biometric fusion for recognition at a distance
Supervised generalized canonical correlation analysis of soft biometric fusion for recognition at a distance
In order to improve biometric system performance,
information fusion becomes a key technique in multi-modal
biometric systems. Multi-modal biometric fusion is
conventionally divided into four levels: sensor level, feature
level, score level and decision level. In this paper, we propose
a supervised generalized canonical correlation (sg-CCA)
method to fuse soft biometric features. The experiments were
performed using a soft biometric database which contains the
human face, body and clothing traits at three different distances.
This paper describes the database and analyses the recognition
performance. Furthermore, it explores the potency of face,
body and clothing for human recognition using sg-CCA fusion
compared with other linear dimensionality reduction fusion
methods. The results demonstrate the superiority of soft
biometric fusion using sg-CCA method for human recognition.
Guo, Bingchen
6e425926-551d-40c2-9c12-e2509d76baa2
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Guo, Bingchen
6e425926-551d-40c2-9c12-e2509d76baa2
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da

Guo, Bingchen, Nixon, Mark and Carter, John (2017) Supervised generalized canonical correlation analysis of soft biometric fusion for recognition at a distance. 8th International Conference on Imaging for Crime Detection and Prevention 2017 (ICDP-2017). 13 - 15 Dec 2017. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In order to improve biometric system performance,
information fusion becomes a key technique in multi-modal
biometric systems. Multi-modal biometric fusion is
conventionally divided into four levels: sensor level, feature
level, score level and decision level. In this paper, we propose
a supervised generalized canonical correlation (sg-CCA)
method to fuse soft biometric features. The experiments were
performed using a soft biometric database which contains the
human face, body and clothing traits at three different distances.
This paper describes the database and analyses the recognition
performance. Furthermore, it explores the potency of face,
body and clothing for human recognition using sg-CCA fusion
compared with other linear dimensionality reduction fusion
methods. The results demonstrate the superiority of soft
biometric fusion using sg-CCA method for human recognition.

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Published date: 2017
Venue - Dates: 8th International Conference on Imaging for Crime Detection and Prevention 2017 (ICDP-2017), 2017-12-13 - 2017-12-15

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Local EPrints ID: 417058
URI: http://eprints.soton.ac.uk/id/eprint/417058
PURE UUID: ecf21baa-2afb-4088-9aba-81e22dfa3b25
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 18 Jan 2018 17:30
Last modified: 07 Oct 2020 02:35

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