• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >Classification schemes based on Partial Least Squares for face identification
【24h】

Classification schemes based on Partial Least Squares for face identification

机译：基于偏最小二乘的人脸识别分类方案

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

Approaches based on the construction of highly discriminative models, such as one-against-all classification schemes, have been employed successfully in face identification. However, their main drawback is the reduction in the scalability once the models for each individual depend on the remaining subjects. Therefore, when new subjects are enrolled, it is necessary to rebuild all models to take into account the new individuals. This work addresses different classification schemes based on Partial Least Squares employed to face identification. First, the one-against-all and the one-against-some classification schemes are described and, based on their drawbacks, a classification scheme referred to as one-against-none is proposed. This novel approach considers face samples that do not belong to subjects in the gallery. Experimental results show that it achieves similar results to the one-against-all and one-against-some even though it does not depend on the remaining subjects in the gallery to build the models. (C) 2015 Elsevier Inc. All rights reserved.
机译：基于高度区分模型的构建方法（例如，针对所有问题的分类方案）已成功用于人脸识别。但是，它们的主要缺点是，一旦每个人的模型依赖于其余主题，可伸缩性就会降低。因此，在注册新主题时，有必要重建所有模型以考虑新个人。这项工作基于用于面部识别的偏最小二乘法解决了不同的分类方案。首先，描述了一种针对所有目标和针对某些目标的分类方案，并基于它们的缺点，提出了一种被称为“一对一”的分类方案。这种新颖的方法考虑了不属于画廊主题的人脸样本。实验结果表明，即使不依赖画廊中剩余的主体来构建模型，它也能与“全部反对”和“反对某些”取得相似的结果。 （C）2015 Elsevier Inc.保留所有权利。

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