Lip localization based on active shape model and gaussian mixture model

Authors:
Kyung Shik Jang;Soowhan Han;Imgeun Lee;Young Woon Woo
Affiliations:
Department of Multimedia Engineering, Dong-Eui University, Pusan, Korea;Department of Multimedia Engineering, Dong-Eui University, Pusan, Korea;Department of Film and Visual Engineering, Dong-Eui University, Pusan, Korea;Department of Multimedia Engineering, Dong-Eui University, Pusan, Korea
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

Citing 10
Cited 0

Active shape models—their training and application

Computer Vision and Image Understanding
Speechreading using probabilistic models

Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Extraction of Visual Features for Lipreading

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
LAFTER: Lips and Face Real-Time Tracker

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Estimation of the Mouth Features Using Deformable Template Matching

ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
An Approach to Statistical Lip Modelling for Speaker Identification via Chromatic Feature Extraction

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Accurate, Real-Time, Unadorned Lip Tracking

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Unsupervised lip segmentation under natural conditions

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Automatic snakes for robust lip boundaries extraction

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.