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Facial feature localization using statistical models and SIFT descriptors | IEEE Conference Publication | IEEE Xplore

Facial feature localization using statistical models and SIFT descriptors


Abstract:

Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by in...Show More

Abstract:

Active Shape Model (ASM) is a powerful statistical tool for image interpretation, especially in face alignment. In the standard ASM, local appearances are described by intensity profiles, and the model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. It suffers from variations of poses, illumination and expressions. In this paper, an improved ASM framework, GentleBoost based SIFT-ASM is proposed. Local appearances of landmarks are originally represented by SIFT (Scale-Invariant Feature Transform) descriptors, which are gradient orientation histograms based representations of image neighborhood. They can provide more robust and accurate guidance for search than grey-level profiles. Moreover, GentleBoost classifiers are applied to model and search the SIFT features instead of the unnecessary assumption of Gaussian distribution. Experimental results show that SIFT-ASM significantly outperforms the original ASM in aligning and localizing facial features.
Date of Conference: 27 September 2009 - 02 October 2009
Date Added to IEEE Xplore: 10 November 2009
CD:978-1-4244-5081-7

ISSN Information:

Conference Location: Toyama, Japan

References

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