A modular approach to facial feature segmentation on real sequences

Votsis, GN; Drosopoulos, AI; Kollias, SD

dc.contributor.author	Votsis, GN	en
dc.contributor.author	Drosopoulos, AI	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T01:18:32Z
dc.date.available	2014-03-01T01:18:32Z
dc.date.issued	2003	en
dc.identifier.issn	0923-5965	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15072
dc.subject	Active contours	en
dc.subject	Dominant angle	en
dc.subject	Facial feature extraction	en
dc.subject	Feature labeling	en
dc.subject	Optimal segmentation	en
dc.subject	Seed growing	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Animation	en
dc.subject.other	Face recognition	en
dc.subject.other	Feature extraction	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Optimization	en
dc.subject.other	Feature labeling	en
dc.subject.other	Image segmentation	en
dc.title	A modular approach to facial feature segmentation on real sequences	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0923-5965(02)00103-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0923-5965(02)00103-0	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	In this paper a modular approach of gradual confidence for facial feature extraction over real video frames is presented. The problem is being dealt under general imaging conditions and soft presumptions. The proposed methodology copes with large variations in the appearance of diverse subjects, as well as of the same subject in various instances within real video sequences. Areas of the face that statistically seem to be outstanding form an initial set of regions that are likely to include information about the features of interest. Enhancement of these regions produces closed objects, which reveal - through the use of a fuzzy system - a dominant angle, i.e. the facial rotation angle. The object set is restricted using the dominant angle. An exhaustive search is performed among all candidate objects, matching a pattern that models the relative position of the eyes and the mouth. Labeling of the winner features can be used to evaluate the features extracted and provide feedback in an iterative framework. A subset of the MPEG-4 facial definition or facial animation parameter set can be obtained. This gradual feature revelation is performed under optimization for each step, producing a posteriori knowledge about the face and leading to a step-by-step visualization of the features in search. © 2002 Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Signal Processing: Image Communication	en
dc.identifier.doi	10.1016/S0923-5965(02)00103-0	en
dc.identifier.isi	ISI:000181043800005	en
dc.identifier.volume	18	en
dc.identifier.issue	1	en
dc.identifier.spage	67	en
dc.identifier.epage	89	en