Please use this identifier to cite or link to this item:
Title: On consistent fusion of multimodal biometrics
Authors: Kung, SY
Mak, MW 
Keywords: Gaussian processes
Biometrics (access control)
Face recognition
Speaker recognition
Issue Date: 2006
Publisher: IEEE
Source: 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : May 14-19, 2006, Centre de Congrès Pierre Baudis, Toulouse, France, p. V-V How to cite?
Abstract: Audio-visual (AV) biometrics offer complementary information sources, and the use of both voice and facial images for biometric authentication has recently become economically feasible. Therefore, multi-modality adaptive fusion, combining audio and visual information, offers an efficient tool for substantially improving the classification performance. In terms of implementation, we propose to integrate an audio classifier (based on Gaussian mixture models) and a visual classifier (based on FaceIT, a commercially available software) into a well-established mixture-of-expert fusion architecture. In addition, a consistent fusion strategy is introduced as a baseline fusion scheme, which establishes the lower bound of the "consistent region" in the FAR-FRR ROC. Our simulation results indicate that the prediction performance of the proposed adaptive fusion schemes fall in the consistent region. More importantly, the notion of consistent fusion can also facilitate the selection of the best modalities to fuse
ISBN: 1-4244-0469-X
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2006.1661468
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Jul 10, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.