This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on lambertian model and local near neighbor feature of the face image, applying to local region within multiple scales. Then, the illumination level is referenced to construct the high performance LEF as well realize adaptive fusion for multiple scales LEFs for the face image, performing JLEF-feature. In addition, the constrain operation is used to remove the useless high-frequency interference, disentangling useful facial feature edges and constructing AJLEF-face. Finally, the effects of the our methods and other state-of-the-art algorithms including deep learning methods are tested on Extended Yale B, CMU PIE, AR as well as our Self-build Driver database (SDB). The experimental results demonstrate that the JLEF-feature and AJLEF-face outperform other related approaches for undersampled face recognition under varying illumination.

中文翻译：

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欠采样人脸识别的自适应多尺度光照不变特征表示

本文提出了一种新颖的光照不变特征表示方法，用于消除欠采样人脸识别中光照变化的影响。首先，提出了一种基于奇异值分解（SVD）的光照水平分类技术来判断输入图像的光照水平。其次，我们基于朗伯模型和人脸图像的局部近邻特征构建对数边缘图特征（LEF），应用于多个尺度内的局部区域。然后，参考光照水平构建高性能LEF，并实现人脸图像多尺度LEF的自适应融合，执行JLEF特征。此外，约束操作用于去除无用的高频干扰，解开有用的面部特征边缘并构建 AJLEF-face。最后，我们的方法和包括深度学习方法在内的其他最先进算法的效果在 Extended Yale B、CMU PIE、AR 以及我们的自建驱动程序数据库 (SDB) 上进行了测试。实验结果表明，JLEF 特征和 AJLEF 人脸在不同光照下的欠采样人脸识别方面优于其他相关方法。