首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition
【24h】

Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition

机译:线性朗伯物体的外观表征,广义光度立体和照明不变的面部识别

获取原文
获取原文并翻译 | 示例

摘要

Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented
机译:传统的光度学立体算法采用具有变化的反照率场的朗伯反射模型,并且仅涉及一个物体的外观。在本文中,我们利用线性Lambertian属性推广了光度学立体算法,以处理一类中所有对象的所有外观,尤其是人脸类。线性朗伯对象是被一组基础对象线性跨越并具有朗伯曲面的对象。线性特性导致等级约束,因此导致观察矩阵的分解,该观察矩阵由在不同的未知照明下的不同对象(例如,不同对象的面部)的示例图像组成。使用一种新颖的线性化算法(考虑了变化的反照率场),使用可积性和对称性约束来完全恢复子空间基。通过将线性朗伯特性的有效性用于仅使用一幅图像的光照不变的面部识别问题,就可以进一步研究该线性朗伯特性的有效性。通过仔细处理兰伯特定律中固有的非线性,将附着的阴影合并到模型中。这使我们能够扩展算法,以在存在多个照明源的情况下执行人脸识别。给出了使用标准数据集的实验结果

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