2D-FACE ALIGNMENT WITH CYCLEGAN FACE AGING IMAGE-TO-IMAGE TRANSLATION

Abstract

Face alignment is one of the pre-processing processes where the face plays a crucial part in image tasks and computer vision. As part of the pre-processing step, it is the first step taken before implementing an image processing task. By aligning face, it is expected to improve the network model performance, because good input data is now represented in the network model. This research aims to see whether pre-processing the input data can improve the network model performance. A 2D-face alignment technique is used to align all the input images. All the input image that is already being aligned is used as the input image for the CycleGAN face aging image-to-image translation model. In this work, the CycleGAN network model is used to translate an image of a young face to their older version and vice versa. The result obtained shows that if the network model is presented with a properly aligned face, it can translate the image into a younger or older version better than when presented with a non-aligned face.