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Assessment of super-resolution for face recognition from very-low resolution images.

机译：从超低分辨率图像中对人脸识别进行超分辨率评估。

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摘要

Super-Resolution (SR) involves the registration of multiple images/frames and reconstruction of a single higher resolution image. The goal of this research is to use multiple, very-low resolution images, such as those produced from a video sequence in a wireless sensor network system, as input to the super-resolution process in a face recognition system. The algorithm used for face recognition is the Fisherfaces method with a nearest neighbor classifier used for the recognition decision. Superresolution consists of two stages, a registration stage and a reconstruction stage.;The testing images were segmented using a simple skin color detection approach. After the cropping, the images were combined into groups of four from a sliding window that would take the current image and the following three images repeating this process by moving to the next image in the sequence and the subsequent three images until the end of the current class, or person, is reached. This same sliding window was used for the super-resolution algorithm using faces from the three people or classes. Each group of four images was used as an input to the Keren registration algorithm where the rotational and translation information was saved that was then entered into the robust super-resolution reconstruction algorithm to create a single high quality image, which was processed by the face recognition algorithm. The methods tested to compare were the average of the same groups of four, the centroid shifted average and the minimum of the four faces in the group. The comparison was based on nearest neighbor classifier and on classification rates. The results were not in favor of the super-resolution method but instead, the centroid shifted average was the best in this study.
机译：超分辨率（SR）涉及多个图像/帧的配准和单个高分辨率图像的重建。这项研究的目的是使用多个非常低分辨率的图像（例如从无线传感器网络系统中的视频序列生成的图像）​​作为面部识别系统中超分辨率过程的输入。用于人脸识别的算法是Fisherfaces方法，其中最近邻分类器用于识别决策。超分辨率包括两个阶段，配准阶段和重建阶段。使用简单的皮肤颜色检测方法对测试图像进​​行分割。裁剪后，将图像从滑动窗口组合为四个窗口，该窗口将获取当前图像，随后的三个图像通过移至序列中的下一个图像以及随后的三个图像直到当前结束来重复此过程班级或个人。使用来自三个人或三类的面孔，将相同的滑动窗口用于超分辨率算法。每组四个图像用作Keren配准算法的输入，其中保存了旋转和平移信息，然后将其输入到鲁棒的超分辨率重建算法中以创建单个高质量图像，该图像由人脸识别处理算法。测试进行比较的方法是四组相同组的平均值，质心平移平均值和组中四个面的最小值。比较是基于最近邻分类器和分类率。结果不利于超分辨率方法，但质心平移平均值是本研究中最好的。

著录项

  • 作者

    Roeder, James Roger.;

  • 作者单位

    The University of Texas at El Paso.;

  • 授予单位 The University of Texas at El Paso.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2009
  • 页码 106 p.
  • 总页数 106
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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