Hierarchical and adaptive deformable model for mouth boundary detection

Ali R. Mirhosseini, Catherine Chen, Kin Man Lam, Hong Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


An automatic algorithm to extract mouth boundaries in human face images is proposed. The algorithm is based on a hierarchical model adaptation scheme using deformable models. The knowledge about the shape of the object is used to define its initial deformable template. Each mouth boundary curve is initially formed based on three control points whose locations are found through an optimization process using a suitable cost functional. The cost functional captures the essential knowledge about the shape for perceptual organization. Two control points are mouth corners, which are used as the initial location of the mouth after an approximate mouth window is found based on locating the head boundary. The model is hierarchically improved in the second stage of the algorithm. Each boundary curve is finely tuned using more control points. An old model is adaptively replaced by a new model only if a secondary cost is further reduced. The results show that model adaptation technique satisfactorily enhances the mouth boundary model in an automated fashion.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE Comp Soc
Number of pages4
Publication statusPublished - 1 Dec 1997
Externally publishedYes
EventProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, United States
Duration: 26 Oct 199729 Oct 1997


ConferenceProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3)
Country/TerritoryUnited States
CitySanta Barbara, CA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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