## 5 Methodology, Experiment Design, and Data Analysis
### Data Analysis
The above results show how a traditional system can behave against the fake and coercion-based attacks on the sensor of a fingerprint-based biometric system and show that the system authenticates an attacker as being legitimate. It also authenticates a legitimate user, whether they are willing or coerced to provide a fingerprint. The next step is to provide the micro-behavioral data using the MFAS to test its capability to protect the users in the case of fake fingerprints or coerced conditions. 3.2 Data Analysis for a Traditional Fingerprint-Based Biometric System Using the Experiment Data with MFAS  
Each of the four conditions of the experiment for the four skin conditions is analyzed according to the MFAS algorithm in this section. First, touch is detected once a border of a fingerprint is present on the glass surface as an initial touch. In all, 960 images per second for 5 s for each of the fingerprints and 1920 images were collected per participant per condition in the four conditions of enrollment. These included fake fingerprints tested by a legitimate user, fake fingerprints tested by an attacker, and coerced fingerprint placement in the four skin and surface conditions. We then applied smoothing, enhancement, digitization, and thinning for every collected image. Each of the steps is performed in sequence to enhance the quality of the images for extracting fingerprint features such as bifurcation and termination and their angles and locations. Figures 7-9 depict the enhancement of the fingerprint images. Although this may seem resource-consuming, it was necessary to have as much information as possible and then test the system with less data and evaluate the impact on the system's accuracy and acceptability. \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 (One Wet)** \\ \hline Enrollment with willingness behavior & 79\% & 100\% & 100\% & 83\% & 90\% & 100\% \\ Fake fingerprint used by the legitimate user & 61\% & 96\% & 91\% & 64\% & 78\% & 93.5\% \\ Fake fingerprint used by an attacker & 64\% & 94\% & 97\% & 65\% & 80\% & 95.5\% \\ Unwillingness fingerprint behavior & 77\% & 100\% & 100\% & 81\% & 89\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of a traditional fingerprint-based biometric system with no MFAS. Figure 6: Fingerprint visibility in mid-wet, wet, and dry conditions. Smoothing was carried out to remove any scars or dirt so the visibility of ridges is clear, as depicted in Figure 7.  
Further enhancement includes filling gaps in ridges, separating parallel ridges, and removing noise using adaptive contextual filters. We used a frequency and orientation-selective Gabor filter, as depicted in Figure 8.  
Digitization was used to remove gray scale pixels using an adaptive threshold where an eight-pixel representation is reduced to one pixel. Then, thinning was applied to facilitate the work for minutia detection, where the width of a ridge is reduced to one pixel, as depicted in Figure 9.  
All of the bifurcation and termination features of a fingerprint are extracted, along with their locations and directions. After the features extraction was carried out, the system reported the sequence of detected features as they appeared. This shows the usual points in a fingerprint that a user starts with and the sequence of those features while being placed on the glass surface when providing their fingerprint. It is hypothesized to be unique when compared to an attacker and serves as a behavioral feature to differentiate when a user is coerced or willing to provide their fingerprint. Figure 10 depicts the detected features at first touch, mid-touch, and final complete fingertip touch on the glass surface,  
Figure 8: Enhancement after applying the Gabor filter on the fingerprints. Figure 7: Fingerprint smoothing. Figure 9: Digitization and thinning.