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首页> 外文期刊>Journal of visual communication & image representation >Efficient facial landmark localization using spatial-contextual AdaBoost algorithm
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Efficient facial landmark localization using spatial-contextual AdaBoost algorithm

机译：使用空间上下文AdaBoost算法进行有效的人脸标志定位

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摘要

Facial landmark detectors can be categorized into global and local detectors. Global facial landmark detectors rely on global statistical relations between landmarks, but do not sufficiently utilize local appearance information, whereas local detectors mainly focus on local appearance attributes of landmarks. Although the AdaBoost algorithm has been successfully employed in object localization, it cannot take advantage of geometric facial feature distribution very well. We propose an AdaBoost algorithm called SC-AdaBoost, which efficiently combines the global knowledge of landmark distribution, the regional shape model, and the local landmark attributes based on a coarse-to-fine strategy. The global prior distribution of landmarks is estimated using a face image set with landmark annotations. First, the face region is detected as a rectangular bounding box using a Haar-like feature-based boosting method, and the global distribution of landmarks is used to determine the facial component regions. Facial landmark localization is roughly performed by regional shape modeling. Posteriors of individual weak classifiers are determined by Gabor wavelet analysis at landmark candidate positions constrained by the regional shape model. SC-AdaBoost is established by empirical risk minimization, which decides the weights for the weak classifiers, and is used for the precise localization. The strength of the proposed approach is shown by extensive experiments using standard face datasets.
机译：面部界标检测器可以分为全局和局部检测器。全局面部地标检测器依赖于地标之间的全局统计关系，但是没有充分利用局部外观信息，而局部检测器主要关注地标的局部外观属性。尽管AdaBoost算法已成功地用于对象定位，但是它不能很好地利用几何面部特征分布。我们提出了一种称为SC-AdaBoost的AdaBoost算法，该算法有效地结合了基于粗糙到精细策略的界标分布的全局知识，区域形状模型和局部界标属性。使用具有地标注释的面部图像集来估计地标的全局先验分布。首先，使用基于Haar的基于特征的增强方法将面部区域检测为矩形边界框，然后使用界标的全局分布来确定面部组成区域。通过区域形状建模粗略地进行人脸标志定位。通过Gabor小波分析在受区域形状模型约束的地标候选位置上确定各个弱分类器的后验。 SC-AdaBoost是通过经验风险最小化建立的，该经验风险最小化确定了弱分类器的权重，并用于精确定位。通过使用标准人脸数据集进行的广泛实验显示了该方法的优势。

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