Integration of some functions to map hairstyles and jawlines together, varying the hairstyle according to the jawline chosen.

'input_0' -> NetEncoder,Ī visual representation of the fitting process is as follows: The changes made in the network are for incorporating the training of appropriate dimensions of input and target vectors. For effectively extracting the hair out of a given picture, an edited version of the Dilated ResNet-105 has been trained on a dataset containing face/hair masks of faces from the CelebA dataset. One of the key features of a human face to keep in mind is the shape of the hair. Human Domain Face/Hair Segmentation mask generation The key principle involved in achieving the goal is cross-domain semantic preservation for effective results. Finally, a mapping function is presented to generate the final results. This report has been divided into two major sections, namely - human analysis and emoji segmentation. The data sets used in this project are available for public use and have been referenced below. This has been achieved by a combination of deep learning and heuristic analysis to produce appropriate results. The aim of this project is to create an algorithm for generating semantically and chromatically similar looking Emojis from faces of human beings.

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Spectacle emoji