## 2 Background and Related Studies
Behavioral biometrics use a user's operational habits and preferences as identify information, and hand gesture is a typical example investigated for authentication to capture biometric information on many smartphone platforms. Many researchers tried to distinguish users based on their input patterns and touch characteristics by extracting information from touch movements on smartphones [PERSON] et al. (2015); [PERSON] et al. (2016); [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2019). These studies concluded that it is possible to positively improve the security of password patterns on touchscreens by integrating behavioral biometrics. Swipe patterns--continuous gestural touch behaviors made on touch screens--have been adopted as a popular password method for user verification or identification on many Android smartphones. In public, however, the swipe passwords on touchscreens may become visible to attackers who engage in shoulder surfing. To prevent over-the-shoulder attacks, [PERSON] et al. [PERSON] et al. (2018) empirically explored the factors for creating secure swipe passwords and found that symmetrical swipe patterns were more difficult to replicate than asymmetrical patterns. They also found out that providing visual feedback of entry gestures on the screen would make the swipe authentication vulnerable to the over-the-shoulder attacks; thus, it is recommended to turn off the visual feedback on the screen when the authentication is made. The behavioral characteristics of users when making a swipe gesture on smartphones, such as pressure strength, rhythm, or angle used, result in discriminative patterns that can be exploited to authenticate users. [PERSON] et al. [PERSON] et al. (2015) explored a statistical approach to the performance across different kinds of swipe operations and revealed that some gestures hold more user-specific information and are more discriminative than others. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) extracted 11 different behavioral characteristics of swipe gestures used on touchscreens, such as duration, length of trajectory, velocity, pressure, and average gravities in touch points on three axes. Among those characteristics, the average x, y, and z gravities were found to be the most discriminative behavioral features. It was also reported that five consecutive swipe movements, instead of a single swipe movement, yielded satisfactory error rates for authentication [PERSON] and [PERSON] (2018). Combined with hand geometry information, behavioral characteristics may provide an accurate and reliable authentication scheme. [PERSON] et al. [PERSON] et al. (2018) designed multi-touch gestures combining hand geometry information and behavioral characteristics to develop a simple and reliable swipe-based authentication method for mobile devices equipped with touchscreens. With only simple touch with fingers straight and together gestures such as vertical, horizontal, and L swipes, [PERSON] et al. [PERSON] et al. (2018) still came up with secure multi-touch authentication that resulted in error rates comparable with those in other studies [PERSON] et al. (2015); [PERSON] et al. (2017); [PERSON] et al. (2018). The behavioral characteristics of gestures provide an additional layer of security to the proposed gesture-based authentication methods since those characteristics are not explicitly visible to the attackers while the gestures and touch movements themselves are graphical and somehow visible. Using eye-gaze patterns for authentication requires eye tracking. [PERSON] et al. [PERSON] et al. (2018) created the \"EyePassShapes\" using eye gestures to construct an EyePIN, and [PERSON] et al. [PERSON] et al. (2018) used gaze patterns by tracking eye-focus points when the users found passwords. Although these authentication methods deter or prevent a wide range of break-ins or hacking, they have been criticized due to the high price of the equipment and system setup that would not be acceptable for practical use.