...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Neural Networks >Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
【24h】

Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines

机译：基于基于总余量的自适应模糊支持向量机的人脸识别

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

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained
机译：本文提出了一种新的分类器，称为基于总余量的自适应模糊支持向量机（TAF-SVM），该分类器处理了应用于向量识别的支持向量机（SVM）中可能出现的几个问题。提出的TAF-SVM不仅通过罚分的模糊化方法解决了离群值导致的过拟合问题，而且还通过使用不同的代价算法来纠正由于数据集非常不平衡而导致的最优分离超平面的偏斜。另外，通过引入总余量算法来代替传统的软余量算法，可以获得较低的泛化误差范围。这三个功能都体现在传统的SVM中，因此提出了TAF-SVM并在线性和非线性情况下对其进行了重新表述。通过使用中原大学（CYCU）多视图和面部识别技术（FERET）人脸数据库这两个数据库，并使用核Fisher判别分析（KFDA）算法提取可辨别的人脸特征，实验结果表明，提出的TAF-就面部识别精度而言，SVM优于SVM。结果还表明，在许多测试中，提出的TAF-SVM可以实现比SVM小的误差方差，从而可以获得更好的识别稳定性

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