Extraction of Visual Features for Lipreading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Recognise Talking Faces
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Joint audio-video processing for biometric speaker identification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A new optimization procedure for extracting the point-based lip contour using active shape model
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Robust lip region segmentation for lip images with complex background
Pattern Recognition
ICA-Based Lip Feature Representation for Speaker Authentication
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Pattern Analysis & Applications
Discriminative Analysis of Lip Motion Features for Speaker Identification and Speech-Reading
IEEE Transactions on Image Processing
Hi-index | 0.01 |
Compared with other traditional biometric features such as face, fingerprint, or handwriting, 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.