Identity verification using speech and face information

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Author(s)
Sanderson, C
Paliwal, KK
Griffith University Author(s)
Year published
2004
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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 ...
View more >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.
View less >
View more >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.
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Journal Title
Digital Signal Processing
Volume
14
Publisher URI
Copyright Statement
© 2004 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.
Subject
Mechanical engineering
Communications engineering