  Recent advances in artificial intelligence promote a wide range of computer
vision applications in many different domains. Digital cameras, acting as human
eyes, can perceive fundamental object properties, such as shapes and colors,
and can be further used for conducting high-level tasks, such as image
classification, and object detections. Human perceptions have been widely
recognized as the ground truth for training and evaluating computer vision
models. However, in some cases, humans can be deceived by what they have seen.
Well-functioned human vision relies on stable external lighting while unnatural
illumination would influence human perception of essential characteristics of
goods. To evaluate the illumination effects on human and computer perceptions,
the group presents a novel dataset, the Food Vision Dataset (FVD), to create an
evaluation benchmark to quantify illumination effects, and to push forward
developments of illumination estimation methods for fair and reliable consumer
acceptability prediction from food appearances. FVD consists of 675 images
captured under 3 different power and 5 different temperature settings every
alternate day for five such days.
