## 1 Introduction  
Recently with the development of advertising and multiple media technology rushing into our daily lives, text and characters are generally taken, in the area of publishing-and-printing and screen display, to show some emotions. The reason why most of these application scenarios express emotions using fonts that are appropriate for the characters is because characters alone cannot express enough emotions to users, especially for hieroglyphs such as Chinese and Japanese. Therefore, during the process of font design, designers tend to incorporate emotional information in them. [1,2] show that characters with specific styles can effectively exert the expression function of emotions, and at the same time, it can be a designed font that can connect characters with people's emotions and resonate with the audience. What is more, modern Chinese character font expressions need to adapt to the different visual pursuits of modern people according to different purposes, environments, and artistic styles. It is very practical to study the relationship between fonts and emotions. Although the fonts' emotion analysis has been well studied for analyzing the expression of the fonts' characteristic, which is deemed necessary to study the emotion that the font shows, quantitative research on the emotional intention is rarely utilized. Considering the discussion above, we propose a questionnaire system to combine both qualitative and quantitative aspects to exactly analyze the specific font attached to the corresponding facial expressions. Different from western characters, common Chinese characters include more than 3500 characters, and each character has various fonts with different thickness, length, and straightness, as well as partial strokes. The design of Chinese fonts is very time-consuming and requires professional skill, artistic sense, and expertise in calligraphy. Compared to Latin alphabet font generation [3], as proposed by [PERSON], the task of Chinese character generation is more complicated and difficult. Most previous works have focused on local strokes of characters rather than the whole. In contrast, Zi2 Fi [4] is directly originated from the Pix2 Pix [5] model, which uses paired images as training data to generate fonts automatically. However, the Zi2 Fi model is hard to train due to mode collapse and instability. Since the performance of Generator and Discriminator is difficult to balance without knowing the metrics of the model training, some generated font images are somewhat blurred. Therefore, we propose the use of Wasserstein distance [6], which can reflect the training performance by measuring the difference between generated images and real images. We also employ Gradient Penalty [7] to enhance the stability of the model and improve the quality of the generated images. Much research has been done to find what to recommend and how to choose appropriate fonts for users. [PERSON] et al. [8] propose a system for automatically generating fonts reflecting Kansei. However, the proposed system operates based on the genetic algorithm that directly operates the parameters related to Kansei engineering many times to creates various kinds of fonts, which is complicated and time-consuming. Therefore, we propose a recommended automatic font generation system, which allows ordinary users who do not need professional font design capabilities to change the font to convey certain emotions. In order to obtain accurate style results, we incorporate a classification loss in the model. The novelty of this research is to generate a new font that more appropriately reflects the emotion contained in the input image. Our main contributions of this paper can be summarized as follows:  
1. We propose and design a questionnaire system to quantitatively and qualitatively study the relationship between fonts and facial expressions. Data analysis shows that the system has high credibility, and the result provides a dataset for further research. 2.