Multibiometric human recognition using 3D ear and face features

We present automatic extraction of local 3D features (L3DF) from ear and face biometrics and their combination at the feature and score levels for robust identification. To the best of our knowledge, this paper is the first to present feature level fusion of 3D features extracted from ear and fronta...

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Main Authors: Islam, Shams, Davies, R., Bennamoun, M., Owens, R., Mian, A.
Format: Journal Article
Published: Pergamon Press 2013
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/23034
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author Islam, Shams
Davies, R.
Bennamoun, M.
Owens, R.
Mian, A.
author_facet Islam, Shams
Davies, R.
Bennamoun, M.
Owens, R.
Mian, A.
author_sort Islam, Shams
building Curtin Institutional Repository
collection Online Access
description We present automatic extraction of local 3D features (L3DF) from ear and face biometrics and their combination at the feature and score levels for robust identification. To the best of our knowledge, this paper is the first to present feature level fusion of 3D features extracted from ear and frontal face data. Scores from L3DF based matching are also fused with iterative closest point algorithm based matching using a weighted sum rule. We achieve identification and verification (at 0.001 FAR) rates of 99.0% and 99.4%, respectively, with neutral and 96.8% and 97.1% with non-neutral facial expressions on the largest public databases of 3D ear and face.
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publishDate 2013
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spelling curtin-20.500.11937-230342017-09-13T13:57:03Z Multibiometric human recognition using 3D ear and face features Islam, Shams Davies, R. Bennamoun, M. Owens, R. Mian, A. 3D face multibiometric multimodal recognition local 3D surface features feature-level fusion score-level fusion We present automatic extraction of local 3D features (L3DF) from ear and face biometrics and their combination at the feature and score levels for robust identification. To the best of our knowledge, this paper is the first to present feature level fusion of 3D features extracted from ear and frontal face data. Scores from L3DF based matching are also fused with iterative closest point algorithm based matching using a weighted sum rule. We achieve identification and verification (at 0.001 FAR) rates of 99.0% and 99.4%, respectively, with neutral and 96.8% and 97.1% with non-neutral facial expressions on the largest public databases of 3D ear and face. 2013 Journal Article http://hdl.handle.net/20.500.11937/23034 10.1016/j.patcog.2012.09.016 Pergamon Press restricted
spellingShingle 3D face
multibiometric multimodal recognition
local 3D surface features
feature-level fusion
score-level fusion
Islam, Shams
Davies, R.
Bennamoun, M.
Owens, R.
Mian, A.
Multibiometric human recognition using 3D ear and face features
title Multibiometric human recognition using 3D ear and face features
title_full Multibiometric human recognition using 3D ear and face features
title_fullStr Multibiometric human recognition using 3D ear and face features
title_full_unstemmed Multibiometric human recognition using 3D ear and face features
title_short Multibiometric human recognition using 3D ear and face features
title_sort multibiometric human recognition using 3d ear and face features
topic 3D face
multibiometric multimodal recognition
local 3D surface features
feature-level fusion
score-level fusion
url http://hdl.handle.net/20.500.11937/23034