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Face alignment based on the multi-scale local features

机译:基于多尺度局部特征的人脸对齐

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Many face recognition algorithms depend on careful positioning of face images into the same canonical pose. Currently, this positioning is usually done by detecting the locations of eyes. And the face images are transformed to the same positions according to the eye coordinates detected. In this paper, we describe a method based on multi-scale local features to achieve face alignment automatically not just dependent on the localizations of two eyes. Given an unaligned face image resulting from a face detector and a set of aligned face images in the data set, we build an automatic transformation mechanism, under which the unaligned face image can be precisely aligned for the following recognition process. Our alignment method improves performance on face recognition tasks, over images aligned by many other algorithms.
机译:许多人脸识别算法都依赖于将人脸图像仔细定位到相同的规范姿势中。当前,这种定位通常是通过检测眼睛的位置来完成的。并且根据检测到的眼睛坐标将面部图像变换到相同位置。在本文中,我们描述了一种基于多尺度局部特征的方法,该方法可以自动实现面部对齐,而不仅取决于两只眼睛的位置。给定由面部检测器产生的未对齐面部图像和数据集中的一组对齐面部图像,我们建立了一种自动转换机制,在该机制下,未对齐的面部图像可以精确地对齐以用于后续的识别过程。我们的对齐方法比通过许多其他算法对齐的图像提高了面部识别任务的性能。

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