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A robust face super-resolution algorithm and its application in low-resolution face recognition system

机译：一种强大的面部超分辨率算法及其在低分辨率面部识别系统中的应用

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摘要

In real-world surveillance scenario, the face recognition (FR) systems pose a lot of challenges due to the captured low-resolution (LR) and noisy probe images. A new face super-resolution (SR) algorithm is proposed to design a recognition model overcoming the challenges of existing FR systems. The proposed SR algorithm inherits the merits of functional-interpolation and dictionary-based SR techniques. The functional interpolation assists in generating more discriminable output, whereas the dictionary-based approach assists in eliminating noise effects from the reconstruction process. Consequently, it produces more discriminable and noise-free high-resolution (HR) images from captured noisy LR probe images, suitable for real-world problems like low-resolution face recognition. The results obtained from the experiments performed on several popular face image datasets including FEI, FERET, and CAS-PEAL-R1 show that the proposed algorithm performs better than all the comparative SR methods.
机译：在现实世界监测场景中，由于捕获的低分辨率（LR）和嘈杂的探针图像，人脸识别（FR）系统构成了很多挑战。提出了一种新的面部超分辨率（SR）算法来设计识别模型克服现有FR系统的挑战。所提出的SR算法继承了功能插值和基于词典的SR技术的优点。功能插值有助于产生更可分辨力的输出，而基于字典的方法有助于消除重建过程的噪声效应。因此，它产生来自捕获的噪声LR探针图像的更可分辨力和无噪声的高分辨率（HR）图像，适用于低分辨率面部识别等现实世界问题。从包括FEI，FERET和CAS-PEAL-R1在包括FEI，FERET和CAS-PEAL-R1的几个流行面部图像数据集中获得的结果表明该算法比所有比较SR方法更好。

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