Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China.
IEEE Trans Image Process. 2011 Aug;20(8):2329-38. doi: 10.1109/TIP.2011.2109729. Epub 2011 Jan 31.
This paper proposes a spectral-graph-based algorithm for face image repairing, which can improve the recognition performance on occluded faces. The face completion algorithm proposed in this paper includes three main procedures: 1) sparse representation for partially occluded face classification; 2) image-based data mining; and 3) graph Laplace (GL) for face image completion. The novel part of the proposed framework is GL, as named from graphical models and the Laplace equation, and can achieve a high-quality repairing of damaged or occluded faces. The relationship between the GL and the traditional Poisson equation is proven. We apply our face repairing algorithm to produce completed faces, and use face recognition to evaluate the performance of the algorithm. Experimental results verify the effectiveness of the GL method for occluded face completion.
本文提出了一种基于谱图的人脸图像修复算法，可提高遮挡人脸的识别性能。本文提出的人脸补全算法包括三个主要步骤：1）基于稀疏表示的部分遮挡人脸分类；2）基于图像的数据挖掘；3）基于图拉普拉斯（GL）的人脸图像补全。所提出框架的新颖之处在于 GL，它源自图形模型和拉普拉斯方程，可以实现对损坏或遮挡的人脸进行高质量的修复。证明了 GL 与传统泊松方程之间的关系。我们应用人脸修复算法生成完成的人脸，并使用人脸识别来评估算法的性能。实验结果验证了 GL 方法在遮挡人脸补全中的有效性。
