Robust lip region segmentation for lip images with complex background
Author(s)
Wang, Shi-Lin
Lau, Wing-Hong
Liew, Alan Wee-Chung
Leung, Shu-Hung
Griffith University Author(s)
Year published
2007
Metadata
Show full item recordAbstract
Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of moustaches and beards. With moustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is ...
View more >Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of moustaches and beards. With moustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is derived automatically which maximizes a cluster validity index. A spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. This facilitates the separation of lip and background pixels that otherwise are inseparable due to the similarity in color. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features like moustaches and beards
View less >
View more >Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of moustaches and beards. With moustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is derived automatically which maximizes a cluster validity index. A spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. This facilitates the separation of lip and background pixels that otherwise are inseparable due to the similarity in color. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features like moustaches and beards
View less >
Journal Title
Pattern Recognition
Volume
40
Issue
12
Publisher URI
Subject
Information systems