Virtual Hairstyle Service Using GANs & Segmentation Mask (Hairstyle Transfer System)

Abstract

The virtual hair styling service, which now is necessary for cosmetics companies and beauty centers, requires significant improvement efforts. In the existing technologies, the result is unnatural as the hairstyle image is serviced in the form of a 'composite' on the face image, image, extracts and synthesizing simple hair images. Because of complicated interactions in illumination, geometrical, and occlusions, that generate pairing among distinct areas of an image, blending features from numerous photos is extremely difficult. To compensate for the shortcomings of the current state of the art, based on GAN-Style, we address and propose an approach to image blending, specifically for the issue of visual hairstyling to increase accuracy and reproducibility, increase user convenience, increase accessibility, and minimize unnaturalness. Based on the extracted real customer image, we provide a virtual hairstyling service (Live Try-On service) that presents a new approach for image blending with maintaining details and mixing spatial features, as well as a new embedding approach-based GAN that can gradually adjust images to fit a segmentation mask, thereby proposing optimal styling and differentiated beauty tech service to users. The visual features from many images, including precise details, can be extracted using our system representation, which also enables image blending and the creation of consistent images. The Flickr-Faces-HQ Dataset (FFHQ) and the CelebA-HQ datasets, which are highly diversified, high quality datasets of human faces images, are both used by our system. In terms of the image evaluation metrics FID, PSNR, and SSIM, our system significantly outperforms the existing state of the art.