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Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

机译：核耦合交叉回归用于低分辨率人脸识别

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摘要

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.
机译：人脸识别（FR）监视应用程序中的低分辨率（LR）将导致LR图像与其高分辨率（HR）模板之间的尺寸不匹配的问题。在本文中，提出了一种称为内核耦合交叉回归（KCCR）的新方法来解决此问题。 KCCR使用内核耦合映射和图形嵌入将LR和HR面部图像投影到统一的非线性嵌入特征空间中，而不是直接在原始观察空间中进行处理。进一步使用光谱回归来提高泛化性能并减少时间复杂度。同时，开发了交叉回归以充分利用HR嵌入来增加LR空间的信息，从而提高识别性能。 FERET和CMU PIE人脸数据库的实验表明，在识别率和时间复杂度方面，KCCR优于现有的基于结构的方法。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第7期|153790.1-153790.9|共9页
  • 作者

    Zhifei Wang; Zhenjiang Miao; Yanli Wan; Zhen Tang;

  • 作者单位

    Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China,Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;

    Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China,Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;

    Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China,Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;

    Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China,Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;

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