论文标题
当地切成薄片 - 韦森斯坦特色套件用于照明不变的面部识别
Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition
论文作者
论文摘要
我们提出了一种在不同的照明条件下获取的数字图像的面部识别的新方法。该方法基于使用ra累积分布变换（R-CDT）对局部梯度分布的数学建模。我们证明，照明变化会导致某些类型的局部图像梯度分布的变形，这些变形在R-CDT域中表达时，可以将其建模为子空间。然后，使用局部梯度分布的R-CDT域中最近的子空间进行面部识别。实验结果表明，在具有挑战性照明条件的几个面部识别任务中，提出的方法优于其他替代方案。实施该方法的Python代码可用，该代码作为软件包Pytranskit的一部分集成。
We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon Cumulative Distribution Transform (R-CDT). We demonstrate that lighting variations cause certain types of deformations of local image gradient distributions which, when expressed in R-CDT domain, can be modeled as a subspace. Face recognition is then performed using a nearest subspace in R-CDT domain of local gradient distributions. Experiment results demonstrate the proposed method outperforms other alternatives in several face recognition tasks with challenging illumination conditions. Python code implementing the proposed method is available, which is integrated as a part of the software package PyTransKit.
