基于关键点逐层重建的人脸图像超分辨率方法

本文提出一种通过基于关键点逐层重建的人脸图像超分辨率方法。该方法考虑到五官和眉毛局部部位的细节对超分重建的重要意义，本文提出对人脸关键点附近局部区域分别训练超分映射函数，并采用逐层迭代重建实现人脸超分的方法，减小直接重建目标图像的难度。针对超分映射函数，本文采取了线性和非线性两种学习方法，其中线性方法采用主成分分析(PCA)，非线性方法采用自编码网络(AutoEncoder)。在超分重建阶段，先采用双线性插值作为初始化，进而利用学习得到的超分映射函数计算局部人脸图像超分，叠加到全局人脸图像，实现整体超分。基于关键点的人脸超分辨率图像质量较其他超分方法在五官的细节上有更好的效果，本文提出的方法在实验数据集上展现了良好的超分结果，验证了低分辨率证件照情况下的人脸识别的有效性。 

Face Super Resolution Method Based on Key Points Layer by Layer

This paper proposed face super resolution (SR) method based on key points layer by layer in order to learn a mapping from low resolution (LR) between high resolution (HR). Considering the importance of rebuilding the details of facial features. This paper trained every mapping function of SR separately based on the key points of face image in processing of this method. Each SR mapping function was iterated to train in order to reduce the difficulty of rebuilding. The paper has proposed the linear and nonlinear mapping function in this method. Linear mapping function was trained by Principal Component Analysis (PCA) and Nonlinear mapping function was trained by AutoEncoder (AE). Using the input data initialized by the method of Bilinear interpolation, the SR method trained each SR mapping function of local patches and superimposed the rebuilt patches on the face image so as to get the final result. This SR method shows efficient in the experiment data set. It has proved to be efficient in processing of face recognition on ID card.