A Bottom-Up Method for Facial Feature Extraction Using Active Shape Models

Facial feature extraction attracts much research interest due to its importance value in applications including human-computer interaction and visual surveillance. Model-based algorithms such as active shape models and active appearance models are efficient for facial feature extraction in color images. However, the complexity of the images and the local nature of active shape models optimization presents a challenge to the localization problem . We propose a new bottom-up method in model fitting stage of ASM, which decompose the whole face model search into several sub-feature search.lt enables the models to be combined with other feature location methods as to get good initialization and lead to more accurate estimates. We demonstrate the experiment results of successfully applying this schema on facial feature extraction.