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Facial landmarks refer to the localization of fundamental facial points onface images. There have been a tremendous amount of attempts to detect thesepoints from facial images however, there has never been an attempt tosynthesize a random face and generate its corresponding facial landmarks. Thispaper presents a framework for augmenting a dataset in a latent Z-space andapplied to the regression problem of generating a corresponding set oflandmarks from a 2D facial dataset. The BEGAN framework has been used to traina face generator from CelebA database. The inverse of the generator isimplemented using an Adam optimizer to generate the latent vector correspondingto each facial image, and a lightweight deep neural network is trained to maplatent Z-space vectors to the landmark space. Initial results are promising andprovide a generic methodology to augment annotated image datasets withadditional intermediate samples.

Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping