High-fidelity Pose and Expression Normalization for face recognition in the wild
High-fidelity Pose and Expression Normalization for face recognition in the wild
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DOI：
10.1109/cvpr.2015.7298679
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Xiangyu Zhu;Zhen Lei;Junjie Yan;Dong Yi;Stan Z. Li
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文献类型：
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作者：
Xiangyu Zhu;Zhen Lei;Junjie Yan;Dong Yi;Stan Z. Li
姿态和表情归一化是在任意条件下恢复人脸的规范视图，从而提高人脸识别性能的关键步骤。理想的归一化方法应该是自动的、与数据库无关的、高保真的，并且应该在保留人脸外观的同时几乎没有伪影和信息丢失。然而，大多数标准化方法无法满足一个或多个目标。本文提出了一种基于3DMM的高保真人脸姿态和表情归一化方法，该方法能够自动生成正面姿态和中性表情的自然人脸图像。具体来说，我们首先提出了一个地标行进的假设来描述的姿态变化造成的2D和3D地标之间的不对应性，并提出了一种姿态自适应3DMM拟合算法。其次，我们将整个图像网格化为一个3D对象，并使用保持身份的3D变换来消除姿态和表情变化。最后，我们提出了一种基于Possion编辑的修复方法，以填补自遮挡造成的不可见区域。在Multi-PIE和LFW上的大量实验表明，该方法显着提高了人脸识别性能，并在约束和无约束环境中优于最先进的方法。
Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance. An ideal normalization method is desired to be automatic, database independent and high-fidelity, where the face appearance should be preserved with little artifact and information loss. However, most normalization methods fail to satisfy one or more of the goals. In this paper, we propose a High-fidelity Pose and Expression Normalization (HPEN) method with 3D Morphable Model (3DMM) which can automatically generate a natural face image in frontal pose and neutral expression. Specifically, we firstly make a landmark marching assumption to describe the non-correspondence between 2D and 3D landmarks caused by pose variations and propose a pose adaptive 3DMM fitting algorithm. Secondly, we mesh the whole image into a 3D object and eliminate the pose and expression variations using an identity preserving 3D transformation. Finally, we propose an inpainting method based on Possion Editing to fill the invisible region caused by self occlusion. Extensive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recognition performance and outperforms state-of-the-art methods in both constrained and unconstrained environments.