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Local Zernike Moment and Multiscale Patch-Based LPQ for Face Recognition

机译：基于局部Zernike矩和基于多尺度补丁的LPQ的人脸识别

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摘要

In this paper, a novel feature extraction method combining Zernike moment with multiscale patch-based local phase quantization is introduced, which can deal with the problem of uncontrolled image conditions in face recognition, such as expressions, blur, occlusion, and illumination changes (EBOI). First, the Zernike moments are computed around each pixel other than the whole image and then double moment images are, respectively, constructed from the real and imaginary parts. Subsequently, multiscale patch-based local phase quantization descriptor is utilized for the non-overlapping patches of moment images to obtain the texture information. Afterward, the support vector machine (SVM) is employed for classification. Experimental results performed on ORL, JAFFE, and AR databases clearly show that the LZM-MPLPQ method outperforms the state-of-the-art methods and achieves better robustness against severe conditions abovementioned.
机译：本文提出了一种将Zernike矩与基于多尺度补丁的局部相位量化相结合的新颖特征提取方法，该方法可以解决面部识别中表情，模糊，遮挡和照度变化（EBOI）等不受控制的图像条件问题）。首先，除了整个图像之外，还围绕每个像素计算Zernike矩，然后分别从实部和虚部构造双矩图像。随后，将基于多尺度补丁的局部相位量化描述符用于矩图像的非重叠补丁以获得纹理信息。之后，使用支持向量机（SVM）进行分类。在ORL，JAFFE和AR数据库上进行的实验结果清楚地表明，LZM-MPLPQ方法优于最新方法，并且在上述严酷条件下具有更好的鲁棒性。

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