Physiological and Behavioral Lip Biometrics: A comprehensive study of their discriminative power

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Title Physiological and Behavioral Lip Biometrics: A comprehensive study of their discriminative power
Author Wang, Shi-Lin; Liew, Alan Wee-Chung
Journal Name Pattern Recognition
Year Published 2012
Place of publication United Kingdom
Publisher Elsevier
Abstract Compared with other traditional biometric features such as face, finger print, or hand writing, lip biometric features contain both physiological and behavioral information. Physiologically, different people have different lips. On the other hand, people can usually be differentiated by their talking style. Current research on lip biometrics generally does not distinguish between the two kinds of information during feature extraction and classification and the interesting question of whether the physiological or the behavioral lip features are more discriminative has not been comprehensively studied. In this paper, different physiological and behavioral lip features are studied with respect to their discriminative power in speaker identification and verification. Our experimental results have shown that both the static lip texture feature and the dynamic shape deformation feature can achieve high identification accuracy (above 90%) and low verification error rate (below 5%). In addition, the lip rotation and centroid deformations, which are related to the speaker’s talking mannerism, are found to be useful for speaker identification and verification. In contrast to previous studies, our results show that behavioral lip features are more discriminative in speaker identification and verification compared to physiological features.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.patcog.2012.02.016
Volume 45
Issue Number 9
Page from 3328
Page to 3335
ISSN 0031-3203
Date Accessioned 2012-06-20
Date Available 2013-06-03T04:03:49Z
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/47736
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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