Enhance ASM Based on DCT-SVM for Facial Feature Points Localization

You Jia Fu; Jian Wei Li; Ru Xi Xiang

doi:10.4028/www.scientific.net/AMM.121-126.820

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126Enhance ASM Based on DCT-SVM for Facial Feature...

Enhance ASM Based on DCT-SVM for Facial Feature Points Localization

Abstract:

Focused on the facial feature points localization, the enhance ASM algorithm based on modeling texture by the DCT-SVM is proposed. First, the statistical shape model is built. Then, some key feature points are selected and their texture models are built by the DCT-SVM. In the subsequent searching, the feature points are divided into two classes based on their reliability gained by DCT-SVM detector, by combining the reliable feature points to the shape constraint, the original shape can finally match to the target face. Experiments show the algorithm is robust to the expressions change and can better locate the features than the traditional ASM.

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Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

820-824

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.820

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Online since:

October 2011

Authors:

You Jia Fu, Jian Wei Li, Ru Xi Xiang

Keywords:

Active Shape Model (ASM), Discrete Cosine Transform (DCT), Facial Feature Points Localization, Least-Square Method (LSM), Support Vector Machine (SVM)

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References

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