## 8 Conclusions and Future Work  
In this paper, a micro-behavioral component has been proposed to combat fake fingerprint attacks as a liveness detection method. Further, willing vs. coerced users providing their fingerprints have been studied to investigate the proposed system's capability of detecting such attacks in comparison with the traditional fingerprint-based biometric systems.  
The results support the main hypothesis, which states that \"Micro-behavior measurement of the fingertip as it is placed on the touch-based sensor surface over time, until a fingerprint is fully formed, is a valid mechanism for verifying if the fingerprint is fake or real and if a user is coerced\". This method has a threshold of 72% accuracy that is required to match the fingerprint behavior to differentiate between the attacks and the legitimate user. Further, a reduction in the sample size for evaluation was tested to improve the system's acceptability in terms of the amount of time needed for each assessment, reaching a reduction in MFAS accuracy of 6%; however, it was still enough to differentiate between the attacks and the legitimate user, with the threshold required to be at least 78% with an EER of 0% in the controlled environment and created dataset.  
Future work includes testing the system over time, with various scenarios, including sitting and standing, at a specific urgency level, and at a specific stress level, to study the impact on the behavior model. Further, the system may be tested with a hill-based attack where a robotic arm-based system mimics the behavior of a user and retries until it reaches an acceptable level. Solutions to such problems are on limiting the number of trials and not reporting the matching score; however, studying if a robotic arm may still mimic a user's micro-behavior is of interest in attacking and strengthening the MFAS. Further, since the smartphone used in the experiment was to utilize the slow-motion capability, it is interesting to explore how one can connect smartphones with traditional fingerprint scanners to detect coerced fingerprint placement or use smartphones to authenticate individuals utilizing the slow-motion feature to detect if a user is being coerced to authenticate their identity using their fingerprint. Finally, a hybrid approach between the current technologies that provide fingerprint-based coercion detection and MFAS may limit the vulnerabilities in each system and make it robust. Future work is important in order to strengthen the technology and further help in understanding its advantages and disadvantages.  
Funded by the Deanship of Scientific Research at the University of Tabuk, Grant: S-1443-0033.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
Acknowledgments: The author would like to thank the Deanship of Scientific Research at the University of Tabuk for funding the research. Grant: S-1443-0033 and thank the Sensor Networks and Cellular Systems (SNCS) research center for the support.  
Conflicts of Interest: The author declares no conflict of interest.