## 5 Experimental Results and Analyses  
### Comparison Experiments and Results  
In Section 4, we illustrate our approach and the detailed model of generating fonts to reflect emotions. We divide the task into three parts. The first one is the expression recognition module, which is up to the state-of-the-art commonly used in expressions recognition on FER2013 [29]. In this part, the probabilities of seven kinds of expressions including 'anger,' 'disgust,' 'fear,' happiness,' 'neutral,''sadness,' and'surprise' are calculated by the expression recognition module. Then the first two probabilities are adjusted into the standard data elaborated in Section 4.1.1. Intuitively, we can see the results of recognition and the generated font with corresponding emotions shown in Figure 8. The upper part of Figure 8 shows the results of expression recognition and the corresponding information after data processing. The bottom part of Figure 8 shows the characters of the source font and the generated characters mixed with two styles, respectively. The font generation stage is performed into two parts: (1) the training process, and (2) the testing process. In the process of training, we adopt a strategy to split training into a two-step process. First, 27 fonts with 1000 characters are randomly sampled and are fed to the model described in Section 4.1.2; then, we freeze the parameters of the encoder. After that, we choose to fine-tune [4] to the six fonts with 3000 characters that are acquired from the questionnaire. By this way, the encoder learns the characters' structure information with 27,000 characters in the first step, and the dedicated decoder can better focus on the characteristics of the target domain. Those loss functions elaborated in Section 4 are designed to make the proposed model generate high-quality images. We also confirm that the  
Figure 8: The results of expression recognition and the generated font with corresponding emotions. When inputting a facial image with specific emotions, a new font is generated by combining the first two probabilities of the emotions. two-timescale update rule (TTUR) [33] is effective, and we advocate using it specifically to address slow learning in regularized discriminators. We set the \(\alpha=100,\ \beta=0.5,\phi=15,\) and \(\gamma=1\) during the first step, and \(\alpha=300,\ \beta=0.5,\)\(\phi=150,\) and \(\gamma=0.9\) during the second step. We employ the style information \(s\) and classification information \(f\) in these two steps of the training process. In the process of the test, the guided signal c is embedded into the generator G to synthesize the specific style of font. The guided signal c is adjusted from first two probabilities that are recognized in the process of expression recognition. The source font is Heiti style. In our experiments, the EG-GAN model and EG-GAN [1] model are employed, respectively. \"EG-GAN 1\" represents the EG-GAN model without adopting the strategy of Gradient Penalty. Intuitively, compared with the Zi2 Zi model, ours can successfully generate multiple style fonts with higher quality. The comparison results are shown in Figure 9.  
Moreover, some evaluation metrics, structural similarity index (_SSIM_) [34,35], and peak signal-to-noise ratio (_PSNR_) [35], to show the quality of synthesized images, are employed in our experiment. _SSIM_ is used to evaluate the aspects of luminance, contrast, and structure between two images. The higher the _SSIM_ score, the clearer the description of image distortion. _PSNR_ is a ratio between the real images and the reconstructed images to measure the quality of the images. We calculate the _SSIM_ and the PSNR for grayscale images (8 bits). Input a reference image \(u\) and a test image \(v\), both of size \(M\times N\), the PANR is defined by:  
\[PSNR(u,v)=10 lg\Big{(}255^{2}/MSE(u,v)\Big{)}, \tag{15}\]  
where  
\[MSE(u,v)=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\Big{(}u_{ij}-v_{ij}\Big{)}^{ 2}. \tag{16}\]  
The _MSE_ represents the cumulative squared error.