Robust Face Recognition for Lighting Conditions

Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions

Evaluations of the state-of-the-art of both academic face recognition algorithms and commercial systems have shown that recognition performance of most current technologies degrades due to the variations of illumination. This paper investigates several illumination normalization methods and proposes some novel solutions. The main contribution of this paper includes: (1) A Gamma Intensity Correc...

Face Recognition System under Varying Lighting Conditions

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. Other recognition systems don’t nullify most of the lighting variations. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching and kernel based...

Virtual Subspace Method for Robust Face Recognition Independent of Lighting Conditions

A concept of virtual subspace is introduced for realizing a robust face recognition independent of the lighting conditions. The virtual subspace is a paradoxical concept because it can be constructed even if only one image is taken. Furthermore, the virtual subspace is gradually converged to the real subspace when face images are subsequently taken. The virtual subspace is defined as an eigensp...

Decomposed Eigenface for Face Recognition under Various Lighting Conditions

Face Recognition under Difficult Lighting Conditions and Occlusions

In recent years face recognition has received substantial attention from researchers in biometrics, pattern recognition, and computer vision communities. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies in recent years of research. Many existing methods in face rec...