## 3 Performance Evaluation  
The implementation requires an i3 processor with a 4 GB RAM on MATLAB R2018b using the Windows 7 operating system. The proposed method was compared to related methods, e.g.,SRGAN [28], TRNR [27], and LSR [26]. Here, the time taken analysis is denoted by the training phase image value. Each individual has five different views (left, right, up, down, and frontal views) under the same light conditions. These face images were aligned manually using the locations of three points: centers of the left and right eyeballs and the center of the mouth. Some aligned face images were cropped to \(32\times 24\) pixels for low-resolution face images, and to \(128\times 96\) pixels for high resolution face images. Based on the same training sets, the proposed method was compared to related methods. Each input face image was generated into five different outputs of LR, synthesized LR face images, and HR face images according to the framework. Table 2 demonstrates the list of PSNR values of the hallucinated image with an input face image on the frontal view. The value of \(K\) is 100 and \(\theta=0.1\). Table 3 demonstrates the list of PSNR values of the hallucinated image with an input face image on the up view. The value of \(K\) was 200 and \(\theta=0.2\). Table 4 demonstrates the list of PSNR values of the hallucinated image with an input face image on the left view. The value of \(K\) was 200 and \(\theta=0.2\). Table 5 demonstrates the list of PSNR values of the hallucinated image with an input face image on the right view. The value of \(K\) was 200 and \(\theta=0.2\). Table 6 demonstrates the list of PSNR values of the hallucinated image with an input face image on the right view. The value of \(K\) was 200 and \(\theta=0.2\). The threshold of similarity was used to find the similarity within a collection of objects. It contained several factors to determine scalability and minimize computational cost. In the testing phase, it generated the low-resolution input images to high-resolution images. The semantic class of the image was essential for solving within the inherent class to achieve the improved result. In-depth features can be implemented to increase the accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Views** & **SRGAN** & **TRNR** & **LSR** & **AIHEM** \\ \hline Frontal & 28.78 & 29.61 & 31.52 & 32.93 \\ Up & 27.98 & 28.34 & 29.14 & 30.75 \\ Down & 32.15 & 33.20 & 33.89 & 34.17 \\ Left & 33.91 & 34.61 & 34.65 & 34.68 \\ Right & 31.87 & 32.70 & 33.25 & 34.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of PSNR values of the hallucinated image—down view, \(K=200\), and \(\theta=0.2\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Views** & **SRGAN** & **TRNR** & **LSR** & **AIHEM** \\ \hline Frontal & 31.94 & 32.49 & 32.67 & 32.89 \\ Up & 25.32 & 26.98 & 26.96 & 29.94 \\ Down & 25.11 & 26.59 & 26.71 & 26.80 \\ Left & 30.45 & 31.13 & 31.65 & 32.47 \\ Right & 26.67 & 28.40 & 29.11 & 30.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of PSNR values of the hallucinated image—frontal view, \(K=100\), and \(\theta=0.1\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Views** & **SRGAN** & **TRNR** & **LSR** & **AIHEM** \\ \hline Frontal & 29.55 & 30.04 & 30.84 & 31.30 \\ Up & 31.62 & 33.64 & 33.98 & 34.18 \\ Down & 27.44 & 28.74 & 28.78 & 28.88 \\ Left & 31.53 & 32.92 & 33.56 & 34.68 \\ Right & 30.82 & 31.36 & 31.42 & 31.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of PSNR values of the hallucinated image—up view, \(K=200\), and \(\theta=0.2\). The computational complexity of the proposed system was analyzed using the big oh notation \(\text{O}\big{(}n^{2}\text{K}\text{ M}\big{)}\), for the face hallucination methodology. The pre-processing and alignment methods were crucial to reducing computational complexity. The performance was analyzed using a threshold-based similarity that was 35 times faster than the other methods. The running time for the image in the testing phase was 13.5 s while using the proposed mechanism.