Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
Statistical Facial Feature Extraction and Lip Segmentation
Authors: Mustafa Berkay Yilmaz
Published in:
Publication year: 2009
Abstract: Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications including audio-visual speech recognition, human-computer interaction, emotion recognition, fatigue detection and gesture recognition.
In this thesis, we develop a probabilistic method for facial feature extraction. This technique is able to automatically learn location and texture information of facial features from a training set. Facial feature locations are extracted from face regions using joint distributions of locations and textures represented with mixtures of Gaussians. This formulation results in a maximum likelihood (ML) optimization problem which can be solved using either a gradient ascent or Newton type algorithm. Extracted lip corner locations are then used to initialize a lip segmentation algorithm to extract the lip contours. We develop a level-set based method that utilizes adaptive color distributions and shape priors for lip segmentation. More precisely, an implicit curve representation which learns the color information of lip and non-lip points from a training set is employed. The model can adapt itself to the image of interest using a coarse elliptical region. Extracted lip contour provides detailed information about the lip shape.
Both methods are tested using different databases for facial feature extraction and lip segmentation. It is shown that the proposed methods achieve better results compared to conventional methods. Our facial feature extraction method outperforms the active appearance models in terms of pixel errors, while our lip segmentation method outperforms region based level-set curve evolutions in terms of precision and recall results.
  download full paper

Home Back