This paper presents a novel filtering method for face recognition under varying illumination. The proposed method starts by normalizing the given input image by gamma transformation. The shadow artifacts in the normalized image are reduced with the decimation free directional filter banks (DDFB). We have used correlation coefficient as a similarity measure for face recognition. Empirically, we have proven that most of the discriminating features in a human face are horizontal in nature. The efficiency of the proposed method is evaluated on two public databases: Yale Face Database B, and the Extended Yale Face Database B. Experimental results demonstrate that the proposed method achieves higher recognition rate under varying illumination conditions in comparison with some other existing methods.
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机译：本文提出了一种在变光照下用于人脸识别的新滤波方法。所提出的方法开始于通过伽马变换对给定的输入图像进行归一化。归一化图像中的阴影伪影可通过无抽取定向滤波器组（DDFB）减少。我们已经使用相关系数作为面部识别的相似性度量。根据经验，我们已经证明人脸中的大多数区别特征在本质上都是水平的。在两个公共数据库Yale Face Database B和Extended Yale Face Database B上评估了该方法的效率。实验结果表明，与其他现有方法相比，该方法在变化的光照条件下具有更高的识别率。
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