  This work presents a method to predict a geometric surface image from a
photograph to assist in image recognition. To recognize objects, several images
from different conditions are required for training a model or fine-tuning a
pre-trained model. In this work, a geometric surface image is introduced as a
better representation than its color image counterpart to overcome lighting
conditions. The surface image is predicted from a color image. To do so, the
geometric surface image together with its color photographs are firstly trained
with Generative Adversarial Networks (GAN) model. The trained generator model
is then used to predict the geometric surface image from the input color image.
The evaluation on a case study of an amulet recognition shows that the
predicted geometric surface images contain less ambiguity than their color
images counterpart under different lighting conditions and can be used
effectively for assisting in image recognition task.
