首页> 外文会议>2011 International Joint Conference on Biometrics >Robust face recognition with class dependent factor analysis
【24h】

Robust face recognition with class dependent factor analysis

机译:可靠的人脸识别,具有基于类别的因素分析

获取原文

摘要

A general framework for face recognition under different variations such as illumination and facial expressions is proposed. The model utilizes the class information in a supervised manner to define separate manifolds for each class. Manifold embeddings are achieved by a nonlinear manifold learning technique. Inside each manifold, a mixture of Gaussians is designated to introduce a generative model. By this way, a novel connection between the manifold learning and probabilistic generative models is achieved. The proposed model learns system parameters in a probabilistic framework, allowing a Bayesian decision model. Experimental evaluations with face recognition under illumination changes and facial expressions were performed to realize the ability of the proposed model to handle different types of variations. Our recognition performances were comparable to state-of art results.
机译:提出了在光照和面部表情等不同变化下进行人脸识别的通用框架。该模型以监督的方式利用类别信息为每个类别定义单独的歧管。流形嵌入是通过非线性流形学习技术实现的。在每个流形内部,指定了高斯混合来引入生成模型。通过这种方式,在流形学习与概率生成模型之间实现了新颖的联系。所提出的模型在概率框架中学习系统参数,从而允许贝叶斯决策模型。通过在光照变化和面部表情下的面部识别进行实验评估,以实现所提出模型处理不同类型变化的能力。我们的识别性能可与最新技术相媲美。

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