## 4 Conclusions  
The proposed Power Spectrum + Prewitt filter FIQA could identify the sharpness of the fingerprint images and increased the average difference in FIQMs from 0.124 to 0.20, which is approximately a 61% improvement among the 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 kernels via Gaussian smoothing. This value corresponds to an improvement in assessment resolution, and good and faulty samples were well identified; the plot shows a clear difference between the two groups of data, and the FIQMs of the good samples were higher than those of the faulty sample, as expected. The conventional Power Spectrum FIQA failed to distinguish between good and faulty fingerprints. Although SSIM had a good performance in sharpness assessment, it is not a suitable candidate for FIQA because it requires reference images; in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Power Spectrum + Prewitt Filter**} & \multicolumn{2}{c}{**Power Spectrum**} & \multicolumn{2}{c}{**SSIM**} \\ \hline Kernel & 3 \(\times\) 3 & 5 \(\times\) 5 & 7 \(\times\) 7 & 3 \(\times\) 3 & 5 \(\times\) 5 & 7 \(\times\) 7 & 3 \(\times\) 3 & 5 \(\times\) 5 & 7 \(\times\) 7 \\ \hline FIQMs(median) & 0.521 & 0.319 & 0.12 & 0.49 & 0.374 & 0.241 & 0.97 & 0.796 & 0.329 \\ \hline Average Difference in FIQMs & 0.20 & & & 0.124 & & & 0.32 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: FIQMs with different levels of blurring for the proposed and conventional algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Power Spectrum + Prewitt Filter** & **Power Spectrum** & **SSIM** \\ \hline Advantage & \begin{tabular}{c} Average difference in FIQMs \\ increases up to 61\%. \\ \end{tabular} & \begin{tabular}{c} A classic approach commonly \\ utilized in the FIQA field. \\ \end{tabular} & \begin{tabular}{c} Good performance in sharpness \\ assessment of FIQA. \\ \end{tabular} \\ \hline Disadvantage & Higher computational demand. & \
\begin{tabular}{c} Lower resolution in FIQMs \\ and could not identify good or \\ faulty samples. \\ \end{tabular} &
\begin{tabular}{c} Requires reference images and failed \\ in good or faulty assessment. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of the proposed and conventional algorithm.  
Figure 8: Experimental results of Good/Faulty Assessment: (**a**) box and whisker plot and (**b**) line chart.  
addition, it failed in the good/faulty assessment. The experimental results confirmed that the proposed FIQA outperformed the traditional approaches.  
Conceptualization, Z.-L.T. and M.-H.H.; formal analysis, T.-W.S., C.-C.L., W.-F.L. and Y.-H.T.; investigation, T.-W.S., C.-C.L., W.-F.L. and Y.-H.T.; resources, S.W. and Z.-L.T.; writing--original draft, T.-W.S. and W.-F.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology, Taiwan, under Grant No. MOST 110-2221-E-131-028.  
Not applicable.  
The authors would like to extend their heartfelt thanks for the instrument support at the Advanced Instrument Center of National Yunlin University of Science and Technology.  
The authors declare no conflict of interest.