—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.
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机译：-Active形状模型（ASM）已被证明是用于图像解释的强大工具，尤其是面部图像的特征点定位。原始ASM模型参数估计基于概况遵循高斯分布的假设。由于姿势，照明和表达变化，它的性能总是容易受到失真。在本文中，ASM模型的改进涉及以下两个方面。首先，通过定义旋转因子来提出自适应参数估计方法。其次，地标的局部外观最初由定向边缘幅度（诗）描述符的模式表示，其可以提供比强度轮廓的更强大和准确的搜索引导。使用IMM数据集进行仿真，该IMM数据集包含240个面部图像。实验结果表明，该方法在外部变化下显着优于原始ASM和ASM Plus LBP方法。
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