Since in reality, multiple surfaces show in the view frustum of a pixel along with changes in lighting conditions; therefore, multiple adaptive Gaussians are required.
Hence, multiple images are acquired under different lighting conditions, processed separately, and merged into one.
Our method aims to analyze multiple factors such as subjects' identities and lighting conditions in a set of vectored images.
In case of uneven lighting conditions (e.g., shadows, multiple light sources possibly having different spectral distributions), it is generally not possible to obtain a meaningful automatic color difference measurement/adjustment without a priori knowledge of the lighting conditions.
Due to multiple limitations and difficulties such as occlusion, lighting conditions, and variation of expressions across the population, or even for an individual, having an automatic system helps in creating intelligent visual media for understanding different expressions.
Because we use multiple source images taken from different angles and under different lighting conditions, the patched area may suffer slightly inconsistency in terms of brightness.
However, today, face recognition remains challenging because of large variations in facial image appearance due to multiple factors including facial feature variations among different subjects, viewpoints, lighting conditions, and facial expressions.
Meanwhile, lighting conditions can alter appearances considerably.
It's a bit frustrating, especially in poor lighting conditions.
Measurements were done in similar lighting conditions.
