## 4 Micro-Behavioral Fingerprint Analysis System (MFAS) Design  
Advancements in sensing technology have made it possible to detect micro-movements, multiple movements measured at the micro-level, and, therefore, micro-behavior, that is, unintentional and uncontrolled movements [30; 31]. This capability allows for designing a micro-behavior fingerprint analysis system (MFAS) to strengthen the sensor level of a fingerprint-based biometric technology by detecting fake or coerced fingerprints and to support the hypotheses of this research work. The micro-behavioral fingerprint analysis system (MFAS) consists of seven components and six features, as depicted in Figure 2:  
1. Sensor: The MFAS system is composed of a Samsung Note 20 smartphone camera placed under a clear thin glass surface with 330 dots per inch (dpi) resolution and 960 images per second. 2. Fingerprint capture: The capture component is an automated trigger to capture a print once a fingertip is in the scan frame, prior to touching the glass surface. It captures 960 images per second for 6 s. It captures the first touch of the fingertip on the sensor until the fingerprint is fully formed, with no changes, and then keeps on capturing for another 2 s while the fingerprint is stationary. Figure 2: The MFAS steps to detect a fake vs. a real and a willing vs. a coerced fingerprint. Image enhancement: The component enhances all captured images by applying smoothing, segmentation, enhancement, digitization, and then thinning to allow the system to detect the minutiae on the fingerprint. This step is important in capturing and analyzing the formation of the ridges over time. * Micro-behavioral-based Fingerprint Feature Extraction component: It detects six features from the captured images while a fingerprint is being formed on the surface of the sensor:
* The sequence of features appearing over time and the location of each feature
* Duration of fingerprint formation, acceleration between each captured image, and standard deviation
* The angle of the fingerprint formation
* Contour shape changes detection
* Fingerprint direction of changes
* Fingerprint micro-movement while stationary
* Each of the features will be detailed in the data analysis section of this article and a list of abbreviations is provided in Abbreviations. * Micro-behavior model creation: The MFAS then captures the feature vector and creates a one-to-one behavioral matching model for the user. * Matcher: The matcher component is fed the stored behavioral model and the newly created behavioral model and returns a matching score. * Decision maker: The decision-maker component allows the user to pass to the next checkpoint on the fingerprint-based biometric system if the matching score of micro-behavior is above a specific threshold or rejects the sample on a non-matched behavior basis, which could be due to a fake fingerprint or a coerced submission of a fingerprint. The seven components in Figure 2 depict the micro-behavioral fingerprint analysis system (MFAS), as well as the detected features, to build the fingerprint behavioral model for matching. Figure 3 depicts a typical fingerprint-based biometric system equipped with a liveness detection system, where the liveness detection system stands between the sensor and the feature extractor and can reject a fingerprint if it is fake prior to sending the data from the sensor to the feature extractor. Figure 4 depicts the placement of the proposed micro-behavioral fingerprint analysis system (MFAS), where it stands between the sensor component and the typical liveness detection system and can reject a coerced fingerprint placement prior to forwarding the fingerprint details to the feature extractor. Objective 1 of the process of designing the MFAC system states that \"the goal to design the micro-behavioral fingerprint analysis system\" has been achieved. The next step is to  
Figure 4: The placement of the proposed micro-behavioral fingerprint analysis system.