## 7 Discussions and Limitations  
Although the MFAS shows promising results in differentiating between a legitimate fingerprint and a fake fingerprint when placed by an attacker and shows a capability in differentiating between a fingerprint being placed willingly and one coerced into being placed, the system takes an unacceptable amount of time to authenticate a user. The average data analysis is over 23 s, 7 s of which are required for the behavior capture and an average of 16 s of which are required for the data analysis. Therefore, we re-analyzed the data at  
Figure 13: The overall results, with and without the MFAS, in the four scenarios, where 1 is a legitimate enrollment, 2 is for a fake fingerprint used by the legitimate user, 3 is for a fake fingerprint used by an attacker, and 4 is for an unwilling submission of a legitimate user’s fingerprint. three intervals: the beginning, middle, and last part of a fingerprint placement, which is equivalent to 1.5 s, on average. This was carried out without the analysis of the 2 s after a fingerprint is stationary, which reduced the computational time for the analysis to under 5 s, on average. Although this improves the functionality in terms of the time of the MFAS, it impacts the accuracy of the detection. Table 5 shows the MFAS results after reducing the number of samples used when analyzing the micro-movement behavior of a fingerprint, and Figure 14 depicts the overall results after the reduction. The results show a reduction in the accuracy of the MFAS; yet, it is still capable of detecting a fake fingerprint and a coerced user. With a requirement to increase the threshold to a minimum of 78% instead of 72%, a difference of 6% is required. The trade-off between security and convenience allows for a faster system that is still capable of detecting the attacks. It is important to note that the data were not acquired over a long period of time or with a change in the user's sitting or standing position, and these factors may have an impact on the accuracy of the MFAS. However, this research work is meant to show that the proposed technology shows potential for detecting fake fingerprints and coerced user attacks using micro-behavior analysis. The system has been tested in a controlled environment to test and report its capabilities. The MFAS shows promising results, but it has not been tested in real-life scenarios. Future work is required to strengthen the technology, as discussed in the future work section. Figure 14: Overall results with and without MFAS, including the reduction in samples and its impact on the results of the MFAS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trial** & \multicolumn{4}{c}{**Condition**} & \multicolumn{2}{c}{**Average**} \\ \hline  & **Dry Skin** & **Dry Skin** & **Wet Skin** & **Wet Skin** & **All** & **Optimal** \\  & **Dry Surface** & **Wet Surface** & **Dry Surface** & **Wet Surface** & **All** & **(One Wet)** \\ \hline Enrollment with willingness behavior & 83\% & 90\% & 92\% & 87\% & 88\% & 91\% \\ Fake fingerprint used by the legitimate user & 73\% & 78\% & 77\% & 69\% & 74.25\% & 77.5\% \\ Fake fingerprint used by an attacker & 59\% & 62\% & 68\% & 57\% & 61.5\% & 65\% \\ Unwillingness fingerprint behavior & 64\% & 72\% & 76\% & 62\% & 68.5\% & 74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of a traditional fingerprint-based biometric system using MFAS OCSVM at a reduced sample size for improving the acceptability of the system. Although the authors in [32] proposed a method for detecting the liveness of a fingerprint by analyzing the skin elasticity, which is the closest to our approach among the other research studies in the literature, our approach shows a greater depth, a higher accuracy, and the ability to use the system to detect not just fake fingerprints but also unwilling and coerced placements of a fingerprint.