Fusion of face and speech data for person identity verification
Ben-Yacoub, S.
Abdeljaoued, Y.
Mayoraz, E.
Dalle Molle Inst. for Perceptual Artificial Intelligence;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Sep 1999
Volume: 10,
Issue: 5
On page(s): 1065-1074
ISSN: 1045-9227
References Cited: 36
CODEN: ITNNEP
INSPEC Accession Number: 6362646
Digital Object Identifier: 10.1109/72.788647
Posted online: 2002-08-06 22:37:19.0
Abstract
Biometric person identity authentication is gaining more and more
attention. The authentication task performed by an expert is a binary
classification problem: reject or accept identity claim. Combining
experts, each based on a different modality (speech, face, fingerprint,
etc.), increases the performance and robustness of identity
authentication systems. In this context, a key issue is the fusion of
the different experts for taking a final decision (i.e., accept or
reject identity claim). We propose to evaluate different binary
classification schemes (support vector machine, multilayer perceptron,
C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier)
to carry on the fusion. The experimental results show that support
vector machines and Bayesian classifier achieve almost the same
performances, and both outperform the other evaluated classifiers
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
