# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 1 Introduction  
Mobile usage has grown tremendously in recent years. Mobile usage is surpassing desktop usage, as mobile devices are being widely used for a variety of online tasks, including web surfing, online shopping, mobile banking, and social media [1]. Fast-growing mobile usage has been leaving lots of sensitive private data and information in mobile devices. These data and information need to be protected against unauthorized access of the devices.  
Screen unlock is a popular mechanism used to secure the mobile device from unauthorized access. A number of screen unlock schemes have been provided by popular mobile platforms (iOS and Android), including the slide, PIN, password, pattern, fingerprint, and face. Among them, pattern unlock provided by the Android platform has been widely adopted by mobile users. It was shown in a recent study [2] that about 40% of Android users use pattern unlock to secure their devices. Another study showed that pattern unlock was preferable over the password and PIN by mobile users, although the PIN outperforms patterns in terms of input speed and error rate [3].  
Although pattern unlock is a convenient scheme for protecting the user's device, it is also susceptible to a number of attacks, including guessing attacks [4], shoulder surfing attacks [5], smudge attacks [6], and side-channel attacks [7, 8]. It was shown in [6] that smudge attacks were able to break the pattern up to 68% of the time. The success rate of guessing attacks can be greatly improved from 13.33% to 74.17% when assisted by smudge attacks [4]. The study of [5] showed that patterns are more memorable and easier to be spied on through shoulder surfing attacks compared with PINs.  
[PERSON] et al. [8] developed a computer vision algorithm that can track the user's finger movement to infer the pattern drawn on the screen. Their approach can break over 95% of the patterns in five attempts before the device is automatically locked by the Android system. Thus, there is a great need to enhance the security of pattern unlock to defend the attacks.  
In this paper, we propose a new two-factor screen unlock scheme that incorporates biometric information with patterns to achieve improved security. The proposed scheme not only verifies \"what you possess\" (pattern), but also \"who you are\" (unique biometric information) to authenticate the mobile user. Specifically, we use surface electromyography (sEMG)-based biometric information to enhance the security of pattern unlock. sEMG signals are generated by the electric activities of muscles and are recorded by the electrodes placed on the skin's surface. Since the sEMG signals are electric potentials generated by muscle cells, the same movements of different people will generate different electric potentials. Compared with existing biometric information used for screen unlocking, such as the fingerprint and face, sEMG signals are much harder to be forged, which makes the proposed scheme secure against attacks. Even the attackers can break the patterns using the aforementioned attacks, and they will not be able to forge the sEMG signals to gain access of the mobile device.  
The rest of this paper is organized as follows. The related work on improving the security of pattern unlock and biometric-based mobile user authentication is described in Section 2. Section 3 introduces the background information of pattern unlock and EMG signals. The proposed method and experiment procedure are provided in Section 4. The experimental results are presented in Section 5. Finally, Section 6 concludes the paper.