Adaptive Multiscale Illumination-Invariant Feature Representation for Undersampled Face Recognition
This addresses the problem of face recognition under varying lighting conditions with limited training data, which is incremental as it builds on existing feature-based methods.
The paper tackles illumination variation in undersampled face recognition by proposing an adaptive multiscale illumination-invariant feature representation, which outperforms state-of-the-art methods including deep learning on datasets like Extended Yale B, CMU PIE, AR, and a self-built driver database.
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.