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Extracting facial features by an inhibitory mechanism based on gradient distributions

机译:基于梯度分布的抑制机制提取面部特征

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The extraction of facial features is a fundamental and crucial step in most face detection and recognition systems. Here a set of approaches are proposed for extracting internal and external facial features in a grey-level images containing single or multiple faces of various sizes at different locations without any restriction on the background. These approaches are distinctive in several aspects: the use of some rarely exploited photometric properties as the basis for facial features extraction, a novel metrics on radial symmetry of gradient orientations, an inhibitory mechanism for extracting internal facial features, and a simple mechanism for external ones.
机译:面部特征的提取是大多数面部检测和识别系统的基本和关键步骤。这里提出了一组方法,用于在不同位置处的包含单个或多个面积的单个或多个面部的灰度图像中提取内部和外部面部特征,而没有任何限制背景。这些方法在若干方面是独特的:使用一些很少利用的光度特性作为面部特征提取的基础,一种新的度量对梯度取向的径向对称性,提取内部面部特征的抑制机制,以及外部外部特征的简单机制。

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