High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN

Abstract

Facial skin condition is perceived as a vital indicator of the person’s apparent age, perceived beauty, and degree of health. Machine-learning-based software analytics on facial skin conditions can be a time- and cost-efficient alternative to the conventional approach of visiting facial skin care shops or dermatologist’s offices. However, the conventional CNN-based approach is shown to be limited in the diagnosis performance due to the intrinsic characteristics of facial skin problems. In this paper, the technical challenges in facial skin problem diagnosis are first addressed, and a set of 5 effective tactics are proposed to overcome the technical challenges. A total of 31 segmentation models are trained and applied to the experiments of validating the proposed tactics. Through the experiments, the proposed approach provides 83.38% of the diagnosis performance, which is 32.58% higher than the performance of conventional CNN approach.