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人脸识别中的光照子空间的构造与分析
Alternative TitleConstruction and Analysis of Illumination Subspace in Face Recognition
刘俊
Subtype工学硕士
Thesis Advisor王阳生
2005-05-01
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword光线变化、人脸识别、光照子空间、光照归一化、阴影消除 Illumination Change Face Recognition Illumination Subspace Illumination Normalization Shadow Removal
Abstract随着世界安全形势的变化,人脸识别作为一种方便有效的身份认证技术得到了极大的重视。但在实际应用场景中由于光线的变化,人脸识别系统的性能往往急剧下降。本文以人脸识别中的光线问题为研究对象,从光照子空间的角度对如何减少光线变化对人脸识别的影响进行了深入研究,并取得了一定的研究成果。本文的主要工作及贡献概括如下: 1. 介绍了人脸识别中的光照归一化的思路,从光照子空间的角度分析了现有的基于商图像的光照归一化方法以及一些后续方法所存在的问题,并提出了一种基于全局光照子空间的方法。该方法利用多个人在多种光照条件下的人脸图像来建立光照子空间,从而能够合成出光照条件和外形都与测试图像近似的人脸图像,更好地满足了商图像的假设前提。同时它也能够避免在合成的虚拟光照图像中过多引进训练集的个人信息。 2. 除漫反射成像模型之外,本文在人脸识别的光线问题研究中引进了反射-光照模型,该模型基于固有图像的理论框架,它指出任意图像可以分解为光照图像和反射图像的乘积,其中反射图像与光照无关。因此,人脸识别中的光线问题就转换成寻找人脸图像的适当约束来分解出与光照无关的反射图像的问题。本文分析了一种从一系列图像中分离固有图像的方法,并研究了其在人脸识别中的应用。 3. 为了实现从单幅人脸图像中消除阴影,本文提出了一种基于梯度光照子空间的方法。该方法考虑了人脸的特殊性,在固有图像分离中引进了人脸本身的约束。利用独立主元分析(ICA)对正面光照的人脸训练集进行统计学习,将得到的两个梯度光照子空间作为约束,利用 ICA 图像合成模型消除测试图像的边缘图中的阴影边缘,并重建得到无阴影的反射图像。 总之,本文以光照子空间为切入点,深入研究了人脸图像在光线变化条件下的空间分布特性,从光照归一化和光照消除两种典型的光照预处理方式出发,充分利用人脸光照子空间的特性,分别在两个方面提出了创新性的算法,取得较好效果,其中基于全局光照子空间的方法已申请实用新型专利。
Other AbstractIllumination changes will make the performance of face recognition system decreasing dramatically in real world environments. In this paper, we focus on the illumination problem in face recognition, researching in-depth how to use illumination subspace to alleviate the negative effect of illumination changes on face recognition, and the main contributions are summarized as follows: 1. We analysis the concept of illumination normalization and present a novel global illumination subspace-based method to perform illumination normalization in face recognition. The global illumination subspace is constructed using the face images of multiple persons under multiple illumination conditions as training set, and it can overcome the shortcomings of existing methods. 2. We introduce the intrinsic image framework into the illumination research in face recognition. So, achieving illumination-invariant equals to decomposing a face image into the product of an illumination image and a reflectance image using the constraints of faces. In this paper, we describe a method to decompose the shadow free reflectance image from a sequence of images, and discuss its application in face recognition. 3.We present a novel gradient illumination subspace-based method to derive the reflectance image from a single face image. This method apply the independent component analysis on training face images without shadows to construct two gradient illumination subspaces, which then can be used to removing shadow edges from the edges maps of the test image. After a reconstruction process, a shadow free face image can be derived from a single test image.
shelfnumXWLW875
Other Identifier200228014603554
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6885
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
刘俊. 人脸识别中的光照子空间的构造与分析[D]. 中国科学院自动化研究所. 中国科学院研究生院,2005.
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