a hands-free and easy-to-use interface.A head gesture is a as one-class ensemble classifier,and one-class feature short burst of several discrete and consecutive movements selection,to improve the authentication performance. of the user's head,as illustrated in Fig.1.Motion sensors We prototype our system on Google Glass.We design (i.e.the accelerometer and gyroscope)on Glass are able to experiments to evaluate gesture recognition in different measure and detect all kinds of head movements due to their user activities.We collect a total of around 6000 gesture high electromechanical sensitivity.However,smart eyewear samples from 18 users to evaluate the authentication presents new challenges for head gesture interface design. performance.Our evaluation shows that GlassGesture We need to answer questions such as "What are easy-to- shows accurate gesture recognition.It can reliably accept perform head gestures?","How do we accurately recognize the authorized users and reject attackers. those gestures?","How do we make the system efficient on resource-limited hardware?",and"How does the system reject II.RELATED WORK unauthorized access?"and so on. In this paper,we present GlassGesture,a system aiming Activity Recognition.Researchers have shown that when to improve the usability of Glass by providing a novel user interface based on head gestures.We are the first work, smart device is carried with user,it can provide context information about the user activities [2]-14].However.in this to the authors'knowledge,to consider head-gesture-based recognition/authentication problems for smart glasses.First, paper,we are not aiming at improving upon the state-of-the-art GlassGesture provides head gesture recognition as a form activity recognition systems.We use a simple activity detector, to tune parameters for gesture detection. of user interface.This has several advantages against the current input methods,because head gestures are easy-to- Gesture Recognition.It has been shown that gestures as perform,intuitive,hands-free,user-defined,and accessible for input can be precise,and fast.While there is a broad range the disabled.In some situations,it may be considered inap- of gesture recognition techniques based on vision,wireless propriate or even rude to operate Glass through the provided signal,touch screen [5]7],we focus mainly on motion- touchpad or voice commands.Head gestures in comparison, sensor-based gesture recognition because it is low-cost,com- can be tiny and not easily noticeable to mitigate the social putationally feasible,and easy to deploy on mobile devices [8]. awkwardness.Second,the head gesture user interface can We differ from these works in that we propose a head gesture based interface for smart glasses.And we carefully design authenticate users.In particular,head gestures have not been exploited in authentication yet in the literature.We propose the system to work with head gestures which faces different a novel head-gesture-based authentication scheme by using challenges such as noise from user activities,performance on resource-constrained devices.For head gesture recognition. simple head gestures to answer security questions.For exam- ple,we ask user to answer a yes-or-no question,by shaking existing work mainly focuses on vision-based methods [9], (no)or nodding (yes)her head.However,an attacker who while GlassGesture utilizes sensor mounted on user's head. knows the answer to the security questions can still access For gesture recognition on Google Glass,Head Wake Up the device.To mitigate such attacks,we further propose to and Head Nudge [10]are two built-in gesture detectors as leverage unique signatures extracted from such head gestures experimental features which monitor the angle of head.A to identify the owner of the device from other users.Compared similar open-sourced implementation can be found in [111.In to the original,touchpad-based authentication,our proposed contrast,GlassGesture is more advanced which can recognize self-defined,free-form head gestures efficiently and accurately. head-gesture-based authentication is more resistant to shoulder surfing attacks and requires much less effort from the user User Authentication.There has been research on au- In summary,we make the following contributions: thenticating based on the unique patterns they exhibit while For gesture recognition,our system increases the input interacting with phone [12-17]through touch screens and space of the Glass by enabling small,easy-to-perform motion sensors.These systems show that such authentication head gestures.We propose a reference gesture library ex- schemes are less susceptible to shoulder surfing,and,don't clusively for head movements.We utilize activity context require the user to memorize passcode.For authentication on information to adaptively set thresholds for robust gesture Google Glass.work [18]and [19]are touchpad-gesture-based detection.We use a weighted dynamic time warping authentication,which needs continuous user effort to hold up (DTW)algorithm to match templates for better accuracy. fingers on the touchpad.Our work is orthogonal that tries to We speed up the gesture matching with a novel scheme, bring easy authentication to smart glasses using head gestures, which reduces the time cost by at least 55%. which is simple,hands-free,and requires less effort For authentication,we prove that "head gestures can be used as passwords".We design a two-factor authenti- III.GLASSGESTURE SYSTEM DESIGN cation scheme,in which we ask users to perform head gestures to answer questions that show up in the near- In this section,we present the system design of Glass- eye display.To characterize head gestures,we identify a Gesture.First,we give an overview of our system and its set of useful features and propose new features based on architecture.Then we introduce each module and elaborate its peak analyses.We also explore several optimizations such corresponding components.a hands-free and easy-to-use interface. A head gesture is a short burst of several discrete and consecutive movements of the user’s head, as illustrated in Fig. 1. Motion sensors (i.e. the accelerometer and gyroscope) on Glass are able to measure and detect all kinds of head movements due to their high electromechanical sensitivity. However, smart eyewear presents new challenges for head gesture interface design. We need to answer questions such as “What are easy-to￾perform head gestures?”, “How do we accurately recognize those gestures?”, “How do we make the system efficient on resource-limited hardware?”, and “How does the system reject unauthorized access?” and so on. In this paper, we present GlassGesture, a system aiming to improve the usability of Glass by providing a novel user interface based on head gestures. We are the first work, to the authors’ knowledge, to consider head-gesture-based recognition/authentication problems for smart glasses. First, GlassGesture provides head gesture recognition as a form of user interface. This has several advantages against the current input methods, because head gestures are easy-to￾perform, intuitive, hands-free, user-defined, and accessible for the disabled. In some situations, it may be considered inap￾propriate or even rude to operate Glass through the provided touchpad or voice commands. Head gestures in comparison, can be tiny and not easily noticeable to mitigate the social awkwardness. Second, the head gesture user interface can authenticate users. In particular, head gestures have not been exploited in authentication yet in the literature. We propose a novel head-gesture-based authentication scheme by using simple head gestures to answer security questions. For exam￾ple, we ask user to answer a yes-or-no question, by shaking (no) or nodding (yes) her head. However, an attacker who knows the answer to the security questions can still access the device. To mitigate such attacks, we further propose to leverage unique signatures extracted from such head gestures to identify the owner of the device from other users. Compared to the original, touchpad-based authentication, our proposed head-gesture-based authentication is more resistant to shoulder surfing attacks , and requires much less effort from the user. In summary, we make the following contributions: • For gesture recognition, our system increases the input space of the Glass by enabling small, easy-to-perform head gestures. We propose a reference gesture library ex￾clusively for head movements. We utilize activity context information to adaptively set thresholds for robust gesture detection. We use a weighted dynamic time warping (DTW) algorithm to match templates for better accuracy. We speed up the gesture matching with a novel scheme, which reduces the time cost by at least 55%. • For authentication, we prove that “head gestures can be used as passwords”. We design a two-factor authenti￾cation scheme, in which we ask users to perform head gestures to answer questions that show up in the near￾eye display. To characterize head gestures, we identify a set of useful features and propose new features based on peak analyses. We also explore several optimizations such as one-class ensemble classifier, and one-class feature selection, to improve the authentication performance. • We prototype our system on Google Glass. We design experiments to evaluate gesture recognition in different user activities. We collect a total of around 6000 gesture samples from 18 users to evaluate the authentication performance. Our evaluation shows that GlassGesture shows accurate gesture recognition. It can reliably accept the authorized users and reject attackers. II. RELATED WORK Activity Recognition. Researchers have shown that when smart device is carried with user, it can provide context information about the user activities [2]–[4]. However, in this paper, we are not aiming at improving upon the state-of-the-art activity recognition systems. We use a simple activity detector, to tune parameters for gesture detection. Gesture Recognition. It has been shown that gestures as input can be precise, and fast. While there is a broad range of gesture recognition techniques based on vision, wireless signal, touch screen [5]–[7], we focus mainly on motion￾sensor-based gesture recognition because it is low-cost, com￾putationally feasible, and easy to deploy on mobile devices [8]. We differ from these works in that we propose a head gesture based interface for smart glasses. And we carefully design the system to work with head gestures which faces different challenges such as noise from user activities, performance on resource-constrained devices. For head gesture recognition, existing work mainly focuses on vision-based methods [9], while GlassGesture utilizes sensor mounted on user’s head. For gesture recognition on Google Glass, Head Wake Up and Head Nudge [10] are two built-in gesture detectors as experimental features which monitor the angle of head. A similar open-sourced implementation can be found in [11]. In contrast, GlassGesture is more advanced which can recognize self-defined, free-form head gestures efficiently and accurately. User Authentication. There has been research on au￾thenticating based on the unique patterns they exhibit while interacting with phone [12]–[17] through touch screens and motion sensors. These systems show that such authentication schemes are less susceptible to shoulder surfing, and, don’t require the user to memorize passcode. For authentication on Google Glass, work [18] and [19] are touchpad-gesture-based authentication, which needs continuous user effort to hold up fingers on the touchpad. Our work is orthogonal that tries to bring easy authentication to smart glasses using head gestures, which is simple, hands-free, and requires less effort. III. GLASSGESTURE SYSTEM DESIGN In this section, we present the system design of Glass￾Gesture. First, we give an overview of our system and its architecture. Then we introduce each module and elaborate its corresponding components