论文标题
加权低级模型的性能分析，图像稀疏图像直方图在低度照明和遮挡下进行面部识别
Performance analysis of weighted low rank model with sparse image histograms for face recognition under lowlevel illumination and occlusion
论文作者
论文摘要
在广泛的计算机视觉应用中，低级矩阵近似（LRMA）模型的目的是从其降解的观察结果中恢复基础低级别矩阵。最新的LRMA方法 - 强大的主成分分析（RPCA）诉诸使用核标准最小化（NNM）作为非凸等级最小化的凸松弛。但是，NNM倾向于超越等级成分，并平等地对待不同的等级组件，从而限制了其在实际应用中的灵活性。我们使用更灵活的模型，即加权schatten p-norm最小化（WSNM），将NNM推广到schatten p-norm最小化，而权重分配给不同的单数值。所提出的WSNM不仅可以更好地近似原始的低级假设，而且还考虑了不同等级组件的重要性。在本文中，将两种LRMA算法的低率恢复性能进行比较 - RPCA和WSNM在遮挡的人面部图像上进行了比较。分析是对耶鲁数据库和自己数据库的面部图像进行的，其中不同的面部表情，眼镜，不同的照明占面部遮挡。该论文还讨论了通过应用这些算法进行的实验结果观察到的突出趋势。由于低级别的图像有时可能无法充分捕获脸部的细节，因此我们进一步提出了一种新的方法，以使用因此获得的稀疏图像的图像 - 历史图来识别任何给定图像中的个体。广泛的实验结果表明，在定性和定量上，WSNM通过消除面部闭塞而更有效地超过了RPCA的性能，从而提供了恢复的较高PSNR和SSIM的低级图像。
In a broad range of computer vision applications, the purpose of Low-rank matrix approximation (LRMA) models is to recover the underlying low-rank matrix from its degraded observation. The latest LRMA methods - Robust Principal Component Analysis (RPCA) resort to using the nuclear norm minimization (NNM) as a convex relaxation of the non-convex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We use a more flexible model, namely the Weighted Schatten p-Norm Minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives a better approximation to the original low-rank assumption but also considers the importance of different rank components. In this paper, a comparison of the low-rank recovery performance of two LRMA algorithms- RPCA and WSNM is brought out on occluded human facial images. The analysis is performed on facial images from the Yale database and over own database , where different facial expressions, spectacles, varying illumination account for the facial occlusions. The paper also discusses the prominent trends observed from the experimental results performed through the application of these algorithms. As low-rank images sometimes might fail to capture the details of a face adequately, we further propose a novel method to use the image-histogram of the sparse images thus obtained to identify the individual in any given image. Extensive experimental results show, both qualitatively and quantitatively, that WSNM surpasses RPCA in its performance more effectively by removing facial occlusions, thus giving recovered low-rank images of higher PSNR and SSIM.
