  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.
