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The purpose of this study was to identify the makeup preference of users and suggest a method to optimize the makeup style by using the preferred image for each age group through the analysis of BeautyGAN. Through this, you can propose a customized makeup style that suits you, and provide beneficial services to the makeup industry and consumers. In addition, by developing and validating new methods that effectively combine deep learning and vision systems, we aim to innovate makeup-related image conversion technology and contribute to academic and practical advances in this field. For this purpose, reference images suitable for each image were collected to implement image optimization for each age group, the input data reflected the researcher’s image, and the face was aligned and resized, after removing images with low resolution or poor lighting conditions. As a result of the performance evaluation of the BeautyGAN model, it was confirmed that the existing image was 51.26%, which is close to the BeautyGAN image of 38.89%. These results are judged to be able to provide customized makeup style suggestions or adjusted makeup effects that reflect the user’s preferences from an academic point of view, and from a practical point of view, it will be possible to improve the quality of customized beauty services by suggesting makeup styles that suit the characteristics of customers more accurately and quickly.
Lee et al. (Wed,) studied this question.