Abstract
Blur in facial images significantly impedes the efficiency of recognition approaches. However, most existing blind deconvolution methods cannot generate satisfactory results due to their dependence on strong edges, which are sufficient in natural images but not in facial images. In this paper, we represent point spread functions (PSFs) by the linear combination of a set of pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp face image is represented by the linear combination of a set of pre-defined orthogonal face images. In doing so, PSF and EI estimation is simplified to discovering two sets of linear combination coefficients, which are simultaneously found by our proposed coupled learning algorithm. To make our method robust to different types of blurry face images, we generate several candidate PSFs and EIs for a test image, and then, a non-blind deconvolution method is adopted to generate more EIs by those candidate PSFs. Finally, we deploy a blind image quality assessment metric to automatically select the optimal EI. Thorough experiments on the facial recognition technology database, extended Yale face database B, CMU pose, illumination, and expression (PIE) database, and face recognition grand challenge database version 2.0 demonstrate that the proposed approach effectively restores intrinsic sharp face images and, consequently, improves the performance of face recognition.
Abstract (translated)
人脸图像中的模糊严重阻碍了识别方法的效率。然而，现有的盲解卷积方法大多依赖于强边缘，这在自然图像中是足够的，而在人脸图像中是不够的。本文用一组预先定义的正交PSF的线性组合来表示点扩散函数（PSF），同样，用一组预先定义的正交面图像的线性组合来表示估计的本征（EI）锐面图像。在此基础上，将PSF和EI估计简化为发现两组线性组合系数，并通过我们提出的耦合学习算法同时得到。为了使该方法对不同类型的模糊人脸图像具有鲁棒性，对一个测试图像生成多个候选PSF和EIS，然后采用非盲反褶积方法对这些候选PSF生成更多的EIS。最后，我们采用盲图像质量评估指标来自动选择最优的EI。对人脸识别技术数据库、扩展的耶鲁人脸数据库B、CMU姿势、照明和表情（PIE）数据库以及2.0版人脸识别大挑战数据库进行了深入的实验，证明了该方法能够有效地恢复固有的锐利人脸图像，从而提高了该算法的性能。CE认证。
URL
https://arxiv.org/abs/1904.08671
