In this work, we proposed a novel method, called Local Modified Zernike Moment per unit Mass (LMZMPM), for face recognition, which is invariant to illumination, scaling, noise, in-plane rotation, and translation, along with other orthogonal and inherent properties of the Zernike Moments (ZMs). The proposed LMZMPM is computed for each pixel in a neighborhood of size 3 × 3 , and then considers the complex tuple that contains both the phase and magnitude coefficients of LMZMPM as the extracted features. As it contains both the phase and the magnitude components of the complex feature, it has more information about the image and thus preserves both the edge and structural information. We also propose a hybrid similarity measure, combining the Jaccard Similarity with the L1 distance, and applied to the extracted feature set for classification. The feasibility of the proposed LMZMPM technique on varying illumination has been evaluated on the CMU-PIE and the extended Yale B databases with an average Rank-1 Recognition (R1R) accuracy of 99.8% and 98.66% respectively. To assess the reliability of the method with variations in noise, rotation, scaling, and translation, we evaluate it on the AR database and obtain an average R1R higher than that of recent state-of-the-art methods. The proposed method shows a very high recognition rate on Heterogeneous Face Recognition as well, with 100% on CUFS, and 98.80% on CASIA-HFB.

中文翻译：

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LMZMPM：用于鲁棒人脸识别的每单位质量的局部修改 Zernike 矩


在这项工作中，我们提出了一种称为单位质量局部修改泽尼克矩（LMZMPM）的新颖方法，用于人脸识别，该方法对照明、缩放、噪声、面内旋转和平移以及其他正交和固有的不变。 Zernike 矩 (ZM) 的属性。所提出的 LMZMPM 针对大小为 3 × 3 的邻域中的每个像素进行计算，然后将包含 LMZMPM 的相位和幅度系数的复杂元组视为提取的特征。由于它包含复杂特征的相位和幅度分量，因此它具有更多有关图像的信息，从而保留了边缘和结构信息。我们还提出了一种混合相似性度量，将 Jaccard 相似度与 L1 距离相结合，并将其应用于提取的特征集进行分类。所提出的 LMZMPM 技术在不同光照下的可行性已在 CMU-PIE 和扩展的 Yale B 数据库上进行了评估，平均 Rank-1 识别 (R1R) 准确度分别为 99.8% 和 98.66%。为了评估该方法在噪声、旋转、缩放和平移变化下的可靠性，我们在 AR 数据库上对其进行了评估，并获得了比最近最先进的方法更高的平均 R1R。该方法在异构人脸识别上也显示出非常高的识别率，在 CUFS 上为 100%，在 CASIA-HFB 上为 98.80%。