## 2 Background and Related Studies  
Most authentication schemes request the users to prove possession of either secret knowledge, such as a password or personal identification number (PIN), or a physical token, like an ID card [6]. RFID has been widely used for user identification and authentication systems [1; 2; 6; 7]. RFID is a relatively new type of physical token used for basic forms of authentication processes, mostly to grant access at entry points of an automated system. For example, it provides a convenient method of authentication for many automated security systems in home networks and at entry points of private corporate environments. However, single-layered security systems using RFID have shown a lack of security with untrusted usage of a stolen ID tag, even with a personal photo on it. Access by keying in correct secret codes can only be made using a certain type of interface. Traditional authentication methods with alphanumeric passwords for proving secret knowledge have been criticized for being vulnerable to shoulder-surfing and replay attacks. To prevent this risk, many alternative approaches to retain ease of use while increasing security have been investigated. One of those approaches was to utilize pattern recognition [5; 8; 9; 10; 11]. Gesture patterns, eye-gaze patterns, and facial patterns have also been used as personal security codes. Studies based on gesture recognition require two key components: (1) acceleration sensors to obtain gesture pattern data and (2) robust probabilistic inference algorithms to analyze the data gathered and classify them into gestures. Since acceleration sensors have the advantage of small size, robustness, and convenience to be embedded into any wearable or portable devices over other sensors, they have been one of the main sensor selections for arbitrary activity recognition through gestures. With an acceleration sensor, for example, a mobile phone can be a practical authentication device that creates a password from discrete gestures [12; 13]. Even though it was reported that a text-based PIN is superior to a gesture passcode in terms of memory retention [12], since text can contain meanings or personal context in a limited manner, gesture passcodes using a mobile phone showed a possibility to utilize a user's kinesthetic memory to effectively recall the passcode. In selecting gesture-based patterns for a passcode, in a similar study using the Wii remote [14], users seemed to be concerned with the practical use of gestures and selected patterns based on such factors as \"easy to remember\", \"convenient movement\", and \"fast to perform\". However, gesture-based passwords can cause security problems for users since gestures are likely to be more visible than text. Since gestures are subject to easy observation and hence lack robustness against leakage, other types of haptic information have also been explored. Haptic devices can provide very rich information about hand movements in terms of direction, pressure, force, angle, speed, and position of the user's interaction, called behavioral characteristics. Using these haptic features, keystroke dynamics, handwritten signatures, user patterns of mouse movements, and finger motions can be used for characterizing personal attributes. [PERSON] et al. [PERSON] et al. (2015) constructed a simple maze application to test the possibility of haptic authentication, but they reported problems in the reliability and accuracy of the algorithm. Better performance results with probability of correct user identification ranging from 81% to 93% were reported with an improved haptic authentication system to characterize a user with haptic signature analysis [PERSON] et al. (2015); [PERSON] et al. (2015). These studies, however, used a fixed haptic device, such as the Phantom, that cannot be embedded at entry points for an automated security system.