• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Optical engineering >Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation
【24h】

Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation

机译：通过基于Gabor尺度特征的非负稀疏表示对闭塞耳朵进行鲁棒分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The Gabor wavelets have been experimentally verified to be a good approximation to the response of cortical neurons. A new feature extraction approach is investigated for ear recognition by using scale information of Gabor wavelets. The proposed Gabor scale feature conforms to human visual perception of objects from far to near. It can not only avoid too much redundancy in Gabor features but also tends to extract more precise structural information that is robust to image variations. Then, Gabor scale feature-based non-negative sparse representation classification (G-NSRC) is proposed for ear recognition under occlusion. Compared with SRC in which the sparse coding coefficients can be negative, the non-negativity of G-NSRC conforms to the intuitive notion of combing parts to form a whole and therefore is more consistent with the biological modeling of visual data. Additionally, the use of Gabor scale features increases the discriminative power of G-NSRC. Finally, the proposed classification paradigm is applied to occluded ear recognition. Experimental results demonstrate the effectiveness of our proposed algorithm. Especially when the ear is occluded, the proposed algorithm exhibits great robustness and achieves state-of-the-art recognition performance.
机译：Gabor小波已经过实验验证，可以很好地近似皮层神经元的反应。利用Gabor小波的尺度信息，研究了一种新的特征提取方法用于人耳识别。拟议的Gabor尺度特征符合人类从远处到近处对物体的视觉感知。它不仅可以避免Gabor特征中的过多冗余，而且还可以提取对图像变化具有鲁棒性的更精确的结构信息。然后，提出了基于Gabor尺度特征的非负稀疏表示分类（G-NSRC），用于遮挡下的人耳识别。与稀疏编码系数可以为负的SRC相比，G-NSRC的非负性符合将部分组合成一个整体的直观概念，因此与视觉数据的生物学建模更加一致。此外，使用Gabor标度功能会增加G-NSRC的判别能力。最后，将提出的分类范例应用于闭塞的耳朵识别。实验结果证明了该算法的有效性。尤其是当耳朵被遮挡时，所提出的算法表现出很高的鲁棒性，并实现了最新的识别性能。

著录项

  • 来源
    《Optical engineering》 |2014年第6期|061702.1-061702.11|共11页
  • 作者

    Baoqing Zhang; Zhichun Mu; Chen Li; Hui Zeng;

  • 作者单位

    University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing 100083, China;

    University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing 100083, China;

    North China University of Technology College of Information Engineering Beijing 100144, China;

    University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing 100083, 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

    Gabor scale feature; non-negative sparse representation; ear recognition; partial occlusion;

    机译：Gabor标度功能;非负稀疏表示;耳朵识别;部分闭塞;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号