## 1 Introduction
The seventh vulnerability is in attacking the matcher, where a matcher always returns a high matching score despite the true matching score. Finally, the eighth vulnerability is in overriding the final decision, where a decision is always to allow access despite any of the previous steps in a biometric system. A typical solution to attacks 3-8 is applying the challenge-response method to ensure that the data being received are from a trusted unaltered component of the biometric system. All oval shapes in Figure 1 represent the possible attack, and all rectangular shapes represent a typical biometric component to authenticate a user. All inherited vulnerabilities create the potential for successful attacks on any fingerprint-based biometric system to fool the authentication system and provide an impostor access. Since most of the attacks (2-7), except for the sensor-level attacks, are similar to those used against any biometric system and can include solutions such as encryption, the cancellation of biometrics [1], and challenge-response mechanisms [2], we find that the research community has carried out substantial work to strengthen this form of security control. Figure 1: Biometric-based system attacks. However, the sensor-level attacks on fingerprint technologies weaken the technology and require further advancements. Two of the most successful attacks on the sensor level are fake fingerprints, where a fingerprint is fake in its origin, e.g., a gummy finger, a reconstruction of a fingerprint from a stored template [3], and coerced fingerprints, where a legitimate user's willingness to provide a fingerprint is not detected and where a user is forced to provide the fingerprint. Although there is no willingness detection component in fingerprint-based biometric systems, there are Liveliness Detection (LD) measures that have been proposed to combat the threat and to ensure that the fingerprint comes from the real user and not from a reconstructed latent impression left on the sensor. Various techniques for liveness detection (LD) have been proposed; some are physiological, detecting the heartbeat or oxygen level from the finger, and others are image-based analyses detecting the changes in pores over time [4; 5; 6; 7]. Even though the current literature shows a great capability of detecting a fake fingerprint, attackers can still develop ways to deceive the liveness detection components. Simulating a heartbeat and including changes in pores have become easily executed processes that have shown success in fooling the fingerprint-based biometric systems at the sensor level. However, none of the previous approaches took into consideration the micro-behavioral aspects of the fingerprint impression while it is being placed on the sensor over time. None of the previous work evaluated the possibility of evaluating the fingerprint impression shape construction over time as a behavioral measurement to detect a fake fingerprint, where a micro-behavior is an involuntary movement measured in micro-seconds. In this paper, we propose a behavioral measurement of a fingerprint while it is placed on the sensor to improve the liveness detection component and propose a willingness detection component with the behavioral measurement. An attacker may simulate a heartbeat but will not be able to provide the genuine behavior of the user's finger when placed on the sensor to verify the fingerprint. Additionally, the behavior of a user, willing or forced, is expected to differ when analyzing the behavior to detect willingness. Not only do the characteristics of the fingerprint matter, but the behavior of the user's finger while leaving the print and while being placed on the sensor also matter. The proposed components take into account the spread of the fingerprint, the formation of the ridges and valleys, as well as the duration and angle.