¹College of Computer Science, Chongqing University, Chongqing, 400030, China
²College of software, Chongqing University of Posts and Telecommunications Chongqing, 400065, China
³Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Abstract—Active Shape Model (ASM) has been shown to be a powerful tool for image interpretation, especially in feature points localization for face image. The original ASM model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. Its performance is always vulnerable to distortion due to pose, illumination and expression variations. In this paper, the improvement of ASM model concerns the following two aspects. Firstly, an adaptive parameter estimation method is proposed by defining a rotation factor. Secondly, local appearances of landmarks are originally represented by Patterns of Oriented Edge Magnitudes (POEM) descriptors, which can provide more robust and accurate searching guidance than intensity profiles. The simulations are carried out using the IMM dataset, which contains 240 face images. Experimental results show that the proposed method significantly outperforms the original ASM and ASM plus LBP method under exterior variations.

Index Terms—Active shape model (ASM); Patterns of Oriented Edge Magnitudes (POEM); Local binary pattern (LBP); Facial feature localization

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Cite: Lifang Zhou, Bin Fang, Weisheng Li, and Lai Peng, "Facial Feature Localization Using Robust Active Shape Model and POEM Descriptors," Journal of Computers vol. 9, no. 3, pp. 717-724, 2014.