CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Digital Image Processing
Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
Lip-shape Dependent Face Verification
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Combining Evidence in Multimodal Personal Identity Recognition Systems
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Lip Recognition Using Morphological Pattern Spectrum
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A Probabilistic Dynamic Contour Model for Accurate and Robust Lip Tracking
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
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 speaker modeling using perceptually motivated feature
Pattern Recognition Letters
Robust face-voice based speaker identity verification using multilevel fusion
Image and Vision Computing
The use of Speech and Lip Modalities for Robust Speaker Verification under Adverse Conditions
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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A novel approach to extract successful biometrics from mouth visual images is presented in this paper. Visual features are extracted from a sequence of images of speakers' lips while speaking. These features consist of the shape and intensity of pixels around the edge of the lips as well as their dynamics. The features are extracted by using particle filters technique to track the movements of the lips. The lips tracker shows adequate accuracy and ability to maintain lock in different speaking scenarios. Speaker models based on these features are built using Gaussian Mixture Models (GMM) trained through the Expectation-Maximisation (EM) algorithm. Satisfactory results are obtained for text-independent speaker recognition carried out on a video database of 35 individuals. A recognition rate of 82.8% for speaker identification and equal error rate of 18% for speaker verification are achieved using this technique.