## 5 Methodology, Experiment Design, and Data Analysis
### Data Analysis  
5.3.1 Data Analysis for a Traditional Fingerprint-Based Biometric System Using the Experimental Data  
After collecting the behavioral data from the 24 participants in all four trials, enrollment occurred, which involves a willing fingerprint placement, testing their fake fingerprint, an attacker trying to fool the system with another's fake fingerprint, and, finally, the unwillingness of providing a legitimate fingerprint. All of the trials were performed under four conditions using different skin and surface conditions. The dataset was then set and ready for analysis to test how a traditional fingerprint-based biometric system, with and without the MFAS, behaved. We then reported the results. A traditional fingerprint-based biometric system only needs the full images of the fingerprints, fully formed, without the behavioral measurements. It works by enhancing the fingerprint images by applying smoothing, segmentation, enhancement, and digitization and then thinning and extracting the fingerprint features, such as bifurcation and termination. The more features are used, the more accurate the system is and the more resources and time it needs to match a single fingerprint. Therefore, we only trained a neural network on bifurcation and termination with the directions and locations in each fingerprint. The template was then stored in a database, taking advantage of the first trial for all participants. All fingerprints were enrolled into the database successfully with 100% accuracy. Next, we used the fake fingerprint images that were created by legitimate users and placed on the sensor. Only fully formed fingerprints were extracted and enhanced and then matched against the enrolled fingerprint. The same was carried out with the fake fingerprints that were used by the attackers, and the same analysis was performed on the unwilling fingerprint activity carried out at the end of the experiment, which was matched against the stored fingerprint template. The analysis was conducted to test whether the dataset is good enough for a traditional fingerprint-based biometric system and to see if any of the fake fingerprints could gain access for the attackers. The results show that all 24 participants' fingerprints were enrolled successfully into the system and were verified when providing unwilling fingerprints. This meant that, regardless of whether a user is willing to provide their fingerprint or is forced to do so, the system still provides access, as the fingerprint is the same in each condition. Rolling out in overly wet and overly dry conditions, the system still gives 100% accuracy in matching the fingerprints. In the case of fake fingerprints, an average of 93.5% were able to authenticate using their fake fingerprints, and an average of 95.5% were able to authenticate using another's fake fingerprints in optimal wet conditions. Only when the skin or the fingerprinting surface is wet do the fingerprint features become visible, since overly dry or overly wet conditions make the ridges of a fingerprint nonvisible. Figure 6 depicts the impact on the skin and surface conditions for the visibility of a fingerprint. These results show how important it is to incorporate liveness detection to safeguard a fingerprint-based biometric system that can be attacked even with simple Play-Doh. Table 3 shows the results per condition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Trial**} & \multicolumn{4}{c}{**Condition**} \\ \cline{2-5}  & **Dry Skin** & **Dry Skin** & **Wet Skin** & **Wet Skin** \\  & **Dry Surface** & **Wet Surface** & **Dry Surface** & **Wet Surface** \\ \hline Enrollment with willingness behavior & 24 & 24 & 24 & 24 \\ Fake fingerprint used by the legitimate user & 24 & 24 & 24 & 24 \\ Fake fingerprint used by an attacker & 24 & 24 & 24 & 24 \\ Unwillingness fingerprint behavior & 17 & 17 & 17 & 17 \\ Total trials & 89 & 89 & 89 & 89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trials per participant in each of the conditions.