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Decomposition and Visualization of Eigenface for Face Recognition under Various Lighting Conditions

机译：不同光照条件下人脸识别的特征脸分解与可视化

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摘要

It is intended in this paper to construct a recognition method that is more robust against changes of lighting conditions than the conventional method. The method applies orthogonal decomposition and virtualization to the eigenface within the framework of the subspace method. The eigenface is derived by applying principal component analysis (eigenvalue decomposition) to the set of face images for each person under various lighting conditions. In the proposed method, the standard face eigenspace (canonical space) is used, and the face image is orthogonally decomposed into the projection component, which is affected mostly by the lighting variation, and the residual component, which contains other aspects of personality and noise. By analyzing the residual component, the noise component in the input image is eliminated. By the above approach, the eigenspace (eigen-projection) containing the standard face information, and the eigenspace (eigen-resid-ual) containing the individuality, are constructed. To the eigen-projection, the concept of the virtual subspace is applied, and the virtual eigen-projection is constructed. With this procedure, stable recognition is realized using the subspace method, even if only a small number of images (as few as one) are registered.
机译：本文旨在构建一种识别方法，该方法比传统方法更能抵抗光照条件的变化。该方法在子空间方法的框架内将正交分解和虚拟化应用于本征面。通过在各种光照条件下对每个人的脸部图像集应用主成分分析（特征值分解）来得出特征脸。在该方法中，使用标准的人脸特征空间（规范空间），并将人脸图像正交分解为投影分量，该分量主要受光照变化的影响，而剩余分量则包含人格和噪声的其他方面。通过分析残留分量，消除了输入图像中的噪声分量。通过上述方法，构造了包含标准面部信息的本征空间（本征投影）和包含个性的本征空间（本征残差）。将虚拟子空间的概念应用于本征投影，并构建虚拟本征投影。通过该过程，即使仅记录了少量图像（少至一张），也可以使用子空间方法实现稳定的识别。

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