• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2012 IEEE Fifth International Conference on Biometrics: Theory Applications and Systems. >Ear recognition via sparse representation and Gabor filters
【24h】

Ear recognition via sparse representation and Gabor filters

机译：通过稀疏表示和Gabor滤波器进行人耳识别

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

In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing large appearance, pose, and lighting variability, yielded a rank-one recognition rate of 98.46%. The proposed system outperforms the method described in [1], which achieves a recognition rate of 96.88% when evaluated on the same dataset. Moreover, the proposed system was evaluated on a greater number of test images per subject, demonstrating its robustness.
机译：在本文中，我们提出了一种基于稀疏表示的全自动耳朵识别方法。在稀疏表示中，将从每个主题的训练数据中提取的特征用于开发字典。在这项工作中，Gabor滤波器用于特征提取。通过从测试数据中提取特征并使用字典表示测试数据来执行分类。然后根据字典条目在其表示中的参与来确定测试数据的类别。在圣母大学（UND）的G数据集上进行的实验结果包含较大的外观，姿势和光照变化，得出的识别率达到98.46％。所提出的系统优于[1]中描述的方法，该方法在同一数据集上进行评估时可达到96.88％的识别率。此外，在每个对象的大量测试图像上对提出的系统进行了评估，证明了其鲁棒性。

著录项

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