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Robust kernel collaborative representation for face recognition

机译：鲁棒的内核协作表示以实现人脸识别

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摘要

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show vari-eties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facia! expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.
机译：基于表示的面部识别的最大挑战之一是训练样本通常不足。换句话说，训练集通常没有足够的样本来显示由照明，面部表情和姿势引起的高维面部图像的多样性。当测试样本与同一受试者的训练样本明显不同时，识别性能将急剧下降。我们提出了一种基于虚拟样本的鲁棒内核协作表示，用于人脸识别。我们认为虚拟训练集传达了原始训练样本的一些合理且可能的变化。因此，我们设计了一个新的目标函数，以更紧密地匹配从原始和虚拟训练集生成的表示系数。为了进一步提高鲁棒性，我们在内核空间中实现了相应的基于表示的人脸识别。值得注意的是，我们的方法可以使用任何类型的虚拟训练样本。我们使用噪杂的人脸图像获取虚拟人脸样本。可以将噪声近似地看作是各种照明的反射！表情和姿势。我们的工作是一种简单可行的方法，可以获取虚拟面部样本以将高斯噪声（和其他类型的噪声）强加给原始训练样本，以获得原始样本的可能变化。在FERET，Georgia Tech和ORL人脸数据库上的实验结果表明，该方法比两种最新的人脸识别方法（如CRC和Kernel CRC）更可靠。

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  • 来源
    《Optical engineering》 |2015年第5期|053103.1-053103.10|共10页
  • 作者

    Wei Huang; Xiaohui Wang; Yanbo Ma; Yuzheng Jiang; Yinghui Zhu; Zhong Jin;

  • 作者单位

    Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, 210094, China ,Hanshan Normal University, Department of Computer Science and Engineering, Chaozhou 521041, China;

    Hanshan Normal University, Department of Computer Science and Engineering, Chaozhou 521041, China;

    Hanshan Normal University, Department of Mathematics and Statics, Chaozhou 521041, China;

    Hanshan Normal University, Department of Computer Science and Engineering, Chaozhou 521041, China;

    Hanshan Normal University, Department of Computer Science and Engineering, Chaozhou 521041, China;

    Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, 210094, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    kernel; collaborative representation; noise-associated available sample; robust face recognition;

    机译：核心;合作代表;与噪声相关的可用样本;强大的人脸识别;

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