## 6 Results
### MFAS Results  
The results reported on the traditional fingerprint system, using ridge-based matching, show that traditional systems are not capable of detecting fake fingerprint placement by an attacker, where 95.5% of all fake data in optimal sensor and finger wet conditions are authenticated as being legitimate. Neither does it provide the capability of detecting coerced fingerprint placement given that 100% of these fingerprints are authenticated as being legitimate, as shown in Table 3, where a legitimate user at gun-point is forced to provide their fingerprint to authenticate and provide access to an attacker. Figure 12: MFAS Data Analysis Algorithm. After analyzing the data using the MFAS, where the micro-movement behavior is captured while a fingerprint is being placed on the glass surface, the system shows a reduction in fake fingerprints being categorized as legitimate and a reduction in the percentage of matching when a user is willing to or coerced into provide their fingerprint. Table 4 shows the results of the 16 datasets while using the OCSVM classifier. The results show that the MFAS can differentiate between real skin and Play-Doh, even if it is used by a legitimate user, since the spread of the skin has different micro-behavioral characteristics when compared to the user's skin. This has been shown in all 4 skin and surface dry and wet conditions, which achieves the second objective, which states \"To test the first supporting hypothesis: if micro-behavioral fingertips, when placed on the sensor surface, over time show a difference between skin and other material\", and supports the first supporting hypothesis, which states \"Skin and other known fake fingerprint material show a difference when compared with each other\", with a difference of 20% in dry skin-dry surface, 24% in dry skin-wet surface, 23% in wet skin-dry surface, and 29% in wet skin-wet surface, an overall average of 24%, and an optimal condition average of 23.5% between real fingerprints and Play-Doh-based fingerprints used by the legitimate user. The results also show that the MFAS is capable of differentiating between the legitimate user placing their fingerprint vs. an attacker using a Play-Doh-based fingerprint, with a difference of 36% in dry skin-dry surface, 39% in dry skin-wet surface, 33% in wet skin-dry surface, and 36% in wet skin-wet surface, an average difference of 36%, and an average optimal condition of 36%. The results achieve the third objective, which states \"To test the second supporting hypothesis of whether a micro-behavioral fingertip, when placed on the sensor surface, over time shows a difference between the legitimate user placing their fingertip vs. an attacker using a constructed fingerprint\". Further, the results show that the MFAS can differentiate between when a user provides their fingerprint willingly or unwillingly, with a difference of 29% in dry skin-dry surface, 32% in dry skin-wet surface, 27% in wet skin-dry surface, and 35% in wet skin-wet surface, an average difference of 30.75%, and an optimal condition average of 29.5%. The results support the fourth objective, which states \"To test the second supporting hypothesis of whether micro-behavioral fingertips, when placed on the sensor surface, over time show a difference between a legitimate user placing their fingertip and a coerced legitimate user\". Together, the third and fourth objectives' results support the second supporting hypothesis, which states that \"A fingertip, when placed by the legitimate user, shows a difference when compared with a fingertip placed by an attacker, regardless of whether the attacker is using a fake fingerprint or forcing the legitimate user to place their fingertip on the sensor\". The overall results suggest that the MFAS is capable of detecting the liveness factor of the fingerprint or if a user is coerced to submit their fingerprint by using the micro-movement behavior while a fingerprint is placed on the sensor.