  Colorization is the method of converting an image in grayscale to a fully
color image. There are multiple methods to do the same. Old school methods used
machine learning algorithms and optimization techniques to suggest possible
colors to use. With advances in the field of deep learning, colorization
results have improved consistently with improvements in deep learning
architectures. The latest development in the field of deep learning is the
emergence of generative adversarial networks (GANs) which is used to generate
information and not just predict or classify. As part of this report, 2
architectures of recent papers are reproduced along with a novel architecture
being suggested for general colorization. Following this, we propose the use of
colorization by generating makeup suggestions automatically on a face. To do
this, a dataset consisting of 1000 images has been created. When an image of a
person without makeup is sent to the model, the model first converts the image
to grayscale and then passes it through the suggested GAN model. The output is
a generated makeup suggestion. To develop this model, we need to tweak the
general colorization model to deal only with faces of people.
