Identity Verification Using Speech and Face Information

Sanderson, Conrad and Paliwal, Kuldip K. (2005) Identity Verification Using Speech and Face Information. Digital signal processing, 14 5: 449-480. doi:10.1016/j.dsp.2004.05.001

Author Sanderson, Conrad
Paliwal, Kuldip K.
Title Identity Verification Using Speech and Face Information
Journal name Digital signal processing   Check publisher's open access policy
ISSN 1051-2004
Publication date 2005-09-01
Sub-type Article (original research)
DOI 10.1016/j.dsp.2004.05.001
Open Access Status Not yet assessed
Volume 14
Issue 5
Start page 449
End page 480
Total pages 32
Place of publication Duluth, MN
Publisher Elsevier Inc.
Language eng
Subject 080106 Image Processing
080104 Computer Vision
090609 Signal Processing
010401 Applied Statistics
Abstract This article first provides an review of important concepts in the field of information fusion, followed by a review of important milestones in audio–visual person identification and verification. Several recent adaptive and nonadaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the nonadaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions.
Keyword Biometrics
Identity verification
Noise resistance
Face recognition
Speaker recognition
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 83 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 103 times in Scopus Article | Citations
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Created: Wed, 01 Apr 2009, 21:15:27 EST by Laura McTaggart on behalf of School of Information Technol and Elec Engineering