Making face recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical recognition systems. The reasons come from the need for automatic recognitions and security systems. To overcome this problem, we propose a novel illumination compensation method called adaptive high-order singular value decomposition to enhance face images at the preprocessing step of the face recognition system. First, we present an RGB color face image as a third-order tensor. Then, adaptive high-order singular value decomposition is proposed to adjust the core tensor automatically by multiplying three frontal slices of the core tensor with their corresponding compensation weight coefficients while keeping the third inverse factor fixed. The experiments performed on five of the most famous public color face databases, namely CMU-PIE, Color FERET, FEI, LFW, and IJB-C reveal that adaptive high-order singular value decomposition not only yields compensated images that are clear, natural, and smooth but also considerably improves the accuracy and computing time of face recognition.

中文翻译：

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张量补偿彩色人脸识别

使人脸识别在不受控制的光照条件下更加可靠是实际识别系统面临的最重要挑战之一。原因来自对自动识别和安全系统的需求。为了克服这个问题，我们提出了一种称为自适应高阶奇异值分解的新型光照补偿方法，以在人脸识别系统的预处理步骤中增强人脸图像。首先，我们将 RGB 彩色人脸图像呈现为三阶张量。然后，提出了自适应高阶奇异值分解，通过将核心张量的三个正面切片与其相应的补偿权重系数相乘，同时保持第三个逆因子固定，自动调整核心张量。