论文标题
自适应多尺度照明不变的功能表示，用于采样的面部识别
Adaptive Multiscale Illumination-Invariant Feature Representation for Undersampled Face Recognition
论文作者
论文摘要
本文介绍了一种新颖的照明性特征代表方法，用于消除不足采样的面部识别中不同照明的感情。首先，提出了一种基于单数值分解（SVD）的新的照明水平分类技术来判断输入图像的照明水平。其次，我们基于兰伯特模型和面部图像的局部邻居特征构建对数EDGEMAPS特征（LEF），该功能适用于多个尺度内的局部区域。然后，指出照明级别构造了高性能LEF，并实现了针对面部图像的多个尺度LEF的适应性融合，并执行JLEF-FEATURE。此外，约束操作用于删除无用的高频干扰，解开有用的面部特征边缘并构建ajlef-face。最后，我们的方法和其他最先进的算法（包括深度学习方法）的效果在扩展的耶鲁B，CMU PIE，AR以及我们的自我构建驱动程序数据库（SDB）上进行了测试。实验结果表明，在不同照明下，JLEF-Feature和Ajlef-Face在不同的照明下的面部识别的其他相关方法优于其他相关方法。
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.
