# High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN
## 1 Introduction  
Facial skin condition is perceived as a vital indicator of the person's apparent age, perceived beauty, and degree of health. A face with shining, silky, bright, hydrated, and trouble-free skin indicates a high degree of beauty and thus attractiveness, which creates an initial impression of the person. As people get older, their facial skin also ages, revealing symptoms of aging such as wrinkles, age spots, and visible pores. The biological age from his or her facial skin condition can intuitively be predicted. For this reason, people wish to maintain youthful facial skin without aging symptoms.  
The conventional way of assessing our facial skin condition is to visit facial skin care shops or dermatologists' offices. However, this requires a burden of locating the right facial skin clinic, making appointments, and visiting the clinics. In addition, the cost incurred for the visit can be substantial.  
Machine learning-based software analytics on facial skin conditions can be a time- and cost-efficient alternative to the conventional approach of visiting the clinics. In recent years, researchers have applied Convolutional Neural Network (CNN) based deep learning models to diagnose facial skin problems [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. However, current CNN-based approaches have been shown to be limited in delivering a diagnosis with high performance and, hence, limited in their applicability in clinics. This is mainly due to the following technical challenges in diagnosing facial skin problems with CNN models.  
* Detecting small-sized skin problems such as pores, moles, and acne
* Handling the high complexity of detecting about 20 different facial skin problem types
* Handling appearance variability of the same facial skin problem type among people
* Handling appearance similarity of different facial skin problem types
* Handling false segmentations on non-facial areasThe goal of this study is to devise effective software methods to overcome the technical challenges that can be provided as a clinic-level high performance of facial skin diagnosis. This paper is to propose a set of five software tactics that can effectively remedy the challenges and provide a high level of performance in diagnosing facial skin problems. The paper also presents a technical assessment of the proposed methods through experiments and comparison to other approaches.  
The paper is organized as the following. Section 2 is to summarize related works and their contributions. Section 3 is to present the intrinsic limitations of conventional CNN approach to diagnosing facial skin problems. Section 4 is to elaborate the set of five tactics that can effectively overcome the technical limitations and provide a high performance of diagnosis. Section 5 is to present the datasets used for training facial skin diagnosis models and the results of the experiments for evaluating the proposed tactics and comparing to other approaches.  
The contribution of this study is twofold: (1) proposing a set of give tactics that overcoming the intrinsic limitations of CNN models in diagnosing face skin problems and (2) seamlessly integrating the tactics into software implementation. Through a proof-of-concept implementation of the system and experiments with it, the set of proposed tactics is shown to provide 83.38% of the diagnosis, improve the diagnosis performance by 32.58% compared to the conventional CNN approach, and outperform by an average of 17.89% compared to diagnosis models with MobileNetV2, Xception, ResNet, VGG16, and VGG19.  
The proposed facial skin diagnosis system with the tactics can potentially be utilized as a supplementary approach in face skin care clinics and a cost-effective alternative to visiting clinics by individuals.