From clamped local shape models to global shape model

Hui Fang, Jingjing Deng, Xianghua Xie, Phil Grant

Research output: Contribution to conferencePosterpeer-review

3 Citations (Scopus)


Facial fiducial point localization is a crucial step for most facial analysis applications, e.g., face recognition, expression recognition and facial aging simulation. Although state-of-art methods have the ability to provide good salient point location on frontal faces, finding a global solution under large variations caused by off-plane rotations and exaggerated expression changes is still a challenge. In this paper, we present a system with a two-level shape model to facilitate accurate facial fiducial point localization. In the first level, two local component models interact with each other in order to offer novel shape constraints. At the same time, the clamped local shape model provides constrained non-linear shape initialization for better convergence performance of the shape model as a whole. The experimental results confirm that the proposed method is capable of dealing with the face alignment under large shape variations.
Original languageEnglish
Publication statusPublished - 2013
EventInternational conference on image processing - Sydney, Australia
Duration: 1 Jan 2013 → …


ConferenceInternational conference on image processing
Period1/01/13 → …


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