Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions

Robust Face Recognition under various illumination conditions

Kernel Linear Collaborative Discriminant Regression Classification for Face Recognition Using Local Binary Pattern

Binary feature descriptors have been widely used in computer vision field due to their excellent discriminative power and strong robustness, and local binary patterns (LBP) and its variations have proven that they are effective face descriptors. However, the forms of such binary feature descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In...

Face Recognition under Varying Illumination

This paper proposes a novel pipeline to develop a Face Recognition System robust to illumination variation. We consider the case when only one single image per person is available during the training phase. In order to utilize the superiority of Linear Discriminant Analysis (LDA) over Principal Component Analysis (PCA) in regard to variable illumination, a number of new images illuminated from ...

Improvement of illumination-insensitive feathers for face recognition under complex illumination conditions

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is less insensitive to complex illumination variations. Secondl...

Face representation under different illumination conditions

To deal with image variations due to illumination problem, recently Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them had the assumption that the 3D surface normal and albedo are known. This assumption limits this algorithm...