## 5 Methodology, Experiment Design, and Data Analysis
### Data Analysis
and it clearly shows the gradual appearance of fingerprint features over time. However, instead of capturing only three instances, all 960 images per second for 5 s were analyzed to provide a detailed appearance of the sequence of fingerprint features and the location and orientation of each feature, bifurcation, and termination of ridges, where each shape, circle triangle or square, depicts the an instance of the fingerprint where new fingerprint features were detected. Subsequently, the duration between each feature appearing and the newly discovered other features are computed. This allows for computing the acceleration of the fingerprint as it is placed on the glass surface and allows for detecting the first touch-point and the spread of the fingerprint over time, along with at what speed and acceleration. Then, the standard deviation of time between each feature discovered is computed to determine how the deviation changes as the fingerprints are placed on the sensor. Is it placed smoothly with no deviation, indicating comfort or the robotic, automated placement of a fingerprint, or does it fluctuate? The answer may assist in detecting whether a fingerprint is being placed by a legitimate user or an attacker, or whether it is placed with willingness or coercion. This allows for detecting whether the angle of fingerprint formation is from top to bottom or from left to right, noting the specific degree with a predefined threshold that can be adjusted to improve the system's accuracy so that it is not so specific that it denies a legitimate user access and not so general that it allows an attacker access. The angle of formation is detected by comparing the flow of discovered new features as a fingerprint is being placed on the glass surface. Changes in the angle of formation indicate if a fingerprint starts from top to bottom but then slides to the right and left. These detected data points are essential in forming the micro-behavioral model for each user to then compare it with fake fingerprints by the same user or an attacker, or when comparing the willingness-based fingerprint placement against coerced fingerprint placement. The contour of the fingerprint as it is being placed on the glass surface is then computed over time, and the changes in the shape of the contour per frame are computed. This provides the detail of the fingerprint surface shape as well as the detailed micro-behavior of the user. Figure 11 depicts the fingerprint contour detection over time. Finally, the system analyzes the final 1920 frames after a fingerprint is completely placed on the glass surface and reports the micro-movements' directional changes. A fingerprint that is completely still, with no changes at all, may be considered a sign of a fake fingerprint. If this is the first time a user is using the system, the system creates a new template of the overall fingerprint ridge features and creates the micro-behavioral model. It then stores them in the database with the user's ID. If the user is authenticating their identity, the system pulls the two templates--the fingerprint ridge features and the behavioral model--and compares them with the newly submitted fingerprint shape and behavioral model. Figure 10: Sequence of fingerprint features detected over time. For the creation of the ridge-based features template, we stored the ridge type, bifurcation or termination, and the x, y coordinates of the ridges, as well as the angle of the tangent line on the x-axis ridge following a typical fingerprint template, as per the FBI. The type is given 1 bit and the location is given 18 bits of x and y values. The direction of the ending is given 8 bits, for a total of 27 bits, following the Automated Fingerprint Identification System (AFIS). A typical number of 10-100 features is usually detected in each fingerprint, which is sufficient to differentiate individuals.