Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Acoustic-labial Speaker Verification
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Robust Computer Vision for Computer Mediated Communication
INTERACT '97 Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction
Multi-Modal Tracking of Faces for Video Communications
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Neural Network-Based Face Detection
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Improving a GMM speaker verification system by phonetic weighting
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Journal of Cognitive Neuroscience
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Manifold analysis of facial gestures for face recognition
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Exploiting prosodic structuring of coverbal gesticulation
Proceedings of the 6th international conference on Multimodal interfaces
Dynamic visual features for audio-visual speaker verification
Computer Speech and Language
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This paper addresses an issue concerning the current classification of biometrics into either physiological or behavioural. We offer clarification on this issue and propose additional qualifications for a biometric to be classedas behavioural. It is observedth at dynamics play a key role in the qualification of these terminologies. These are illustrated by practical experiments baseda round visual speech. Two sets of speaker recognition experiments are considered: the first uses lip profiles as both a physiological anda behavioural biometric, the second uses the inherent dynamics of visual speech to locate key facial features. Experimental results using short, consistent test andt raining segments from video recordings give recognition error rates as: physiological - lips 2% and face circles 11%; behavioural - lips 15% andv oice 11%.