Abstract
Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.
Highlights
Digital face images have a wide range of applications in the fields of face recognition [1], facial performance capture [2], facial three-dimensional (3D) animation modeling [3], and face fusion [4], and they are the focus of current academic research with broad application prospects.due to human interference, shooting equipment failure, and encoding and decoding during transmission, the original digital image is significantly defective [5], which will cause the loss of facialSymmetry 2020, 12, 190; doi:10.3390/sym12020190 www.mdpi.com/journal/symmetrySymmetry 2020, 12, 190 image feature information and seriously affect the accuracy of face recognition
We locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range
In the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face
Summary
Symmetry 2020, 12, 190 image feature information and seriously affect the accuracy of face recognition. Image inpainting was originally a traditional graphics problem, mainly based on mathematical and physical methods, using the existing information in the image to restore the defective part of the image. For the defective area in the image, starting from the edge of the target area, using the structure of the non-target area and texture information, the unknown area is predicted and patched according to the matching criteria, so that the filled image is visually reasonable and real [6]. Digital image inpainting algorithms can be divided into two categories: structural propagation methods based on partial differential equations (PDEs) [7] and texture synthesis methods based on sample block [8]
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