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Towards resolution invariant face recognition in uncontrolled scenarios

机译：在不受控制的情景中解决不变量的人脸识别

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Face images captured by surveillance cameras usually have poor quality, particularly low resolution (LR), which affects the performance of face recognition seriously. In this paper, we develop a novel approach to address the problem of matching a LR face image against a gallery of relatively high resolution (HR) face images. Existing methods deal with such cross-resolution face recognition problem either by importing the information of HR images to help synthesize HR images from LR images or by applying the discrimination of HR images to help search for a unified feature space. Instead, we treat the discrimination information of HR and LR face images equally to boost the performance. The proposed approach learns resolution invariant features aiming to: (1) classify the identity of both LR and HR face images accurately, and (2) preserve the discriminative information among subjects across different resolutions. We conduct experiments on databases of uncontrolled scenarios, i.e., SCface and COX, and results show that the proposed approach significantly outperforms state-of-the-art methods.
机译：由监控摄像机捕获的面部图像通常具有差的质量，特别是低分辨率（LR），这影响了人脸识别的性能。在本文中，我们开发了一种新的方法来解决与相对高分辨率（HR）面部图像的库匹配的问题匹配LR面部图像的问题。现有方法通过导入HR图像的信息来处理这种跨分辨率面部识别问题，以帮助综合LR图像的HR图像，或者通过应用HR图像的识别来帮助搜索统一的特征空间。相反，我们同样地对待HR和LR面部图像的辨别信息来提高性能。所提出的方法学习解决方案不变特征，旨在：（1）准确地对LR和HR面部图像的身份进行分类，（2）在不同分辨率跨越对象之间的鉴别信息。我们对不受控制的情景数据库进行实验，即SCFACE和COX，结果表明，所提出的方法显着优于最先进的方法。

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