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首页> 外文会议>2016 International Conference on Biometrics >Towards resolution invariant face recognition in uncontrolled scenarios
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Towards resolution invariant face recognition in uncontrolled scenarios

机译：在不受控制的情况下实现分辨率不变的人脸识别

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摘要

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：监视摄像机捕获的面部图像通常质量较差，尤其是低分辨率（LR），这会严重影响面部识别的性能。在本文中，我们开发了一种新颖的方法来解决将LR面部图像与相对高分辨率（HR）面部图像库匹配的问题。现有方法通过导入HR图像的信息以帮助从LR图像合成HR图像或通过应用HR图像的辨别力来帮助搜索统一的特征空间来解决这种交叉分辨率的面部识别问题。相反，我们平等对待HR和LR面部图像的识别信息以提高性能。所提出的方法学习分辨率不变的特征，其目的是：（1）准确地对LR和HR面部图像的身份进行分类，以及（2）保留不同分辨率下对象之间的区别信息。我们在不受控制的方案（即SCface和COX）的数据库上进行了实验，结果表明，所提出的方法明显优于最新方法。

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  • 来源
  • 会议地点 Halmstad(SE)
  • 作者

    Dan Zeng; Hu Chen; Qijun Zhao;

  • 作者单位

    National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University Chengdu, 610065, China;

    National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University Chengdu, 610065, China;

    National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University Chengdu, 610065, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Image resolution; Face; Face recognition; Feature extraction; Training; Probes; Signal resolution;

    机译：图像分辨率人脸人脸识别特征提取训练探针信号分辨率;

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