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

Using colorization as a tool for automatic makeup suggestion