Data Collection Head-Gesture-base Gyroscope Authentication gyroscope readings while the user is performing head gestures Accelerometer Feature in various activities,compared to relatively noisy accelerom- Extractor eter readings.There it is possible to provide head gesture Head Gesture Recognition detection/recognition through the gyroscope data.3)Head Trainins/ Activity Detector Retraining gestures can be used rather frequently by the user.We need an efficient recognition scheme for performance considerations. Gesture Detector Gesture Recognize In summary.we face three challenges in designing this module.1)Head gesture library:There is no library,which Templates Accept/ defines the most suitable head gestures for smart glasses.2) Deny Noise:Sensors on Glass are used to collect head movements, while at the same time may also collecting noise from other Fig.2:System Architecture user activities.This will deteriorate the performance of the gesture recognition.3)Computation:In recognition tasks, A.System Overview computationally-intensive algorithms may need to be called Our system consists of two modules,which together form frequently,resulting in unsatisfactory performance.Therefore, our gesture-based interface.The first module allows users to it must be optimized to be extremely efficient,without sacri- input small gestures using their heads;the second module ficing substantial recognition accuracy. authenticates users based on their head gestures.The archi- Head Gesture Library.We need to provide a head gesture tecture of our system is illustrated in Fig.2,which shows library as reference since head gestures are quite different from that the Gesture Recognition module is the corner stone.We traditional hand gestures.For example,1)head gestures mainly leverage an activity detector to tune the parameters for more consist of rotational movement.2)users moving their heads accurate gesture detection,based on user activity context.An have limited freedom in 3D space.(e.g.usually humans can enrollment submodule is in charge of managing the gesture only look up and down in less than 180.3)In order to convey templates.The gesture recognizer runs the DTW matching more information.we need a new set of head gestures beside algorithm to recognize potential gestures.The gesture-based the traditional ones that are already used (e.g.,shaking for authentication module is built on top of the first module. "no"and nodding for "yes").In light of these constraints,we It extracts features from the raw sensor data for training. develop six basic candidate gesture categories adapted from With trained classifiers,we form a two-step authentication work [8]and [21]:1)nod,2)look up/down/left/right,3)shake. mechanism using simple head gestures to answer secure 4)circle,5)triangle,and 6)rectangle.To clear up confusion questions first and identifying the correct,unique signatures in when drawing (performing,acting out the gesture),we suggest the gesture movement data second.In the following sections, the user move their head just like drawing something in the we present the design details of each module. air in front of themselves using their nose like a pen tip. B.Head Gesture Recognition Gesture Styles Number Easy Frequency Easy Observations and Challenges.We have made some pre- of strokes to perfomm in Fig to repeat Decisicn up and down 3 52 low keep 2 up/down/left/right 49 high 81) keep,repeat 15 sitting 3 left and right 3+ 44 oa keep 4 30 very low neutral k知p 3 22 very low drop .15 6 start points 14 very low drop TABLE I:Head gesture candidates. With the purpose of trying to figure out what gestures are suitable,we performed a simple survey to rank them on how 10.0 20.0 36.0 Time(s) easy each category is to be performed for untrained users.It Fig.3:Collected Sensor Trace:The user sits still for about 17s is important to note that the survey,and all data collections in then stands up and walks for about 10s,then runs for a few seconds the entire paper,have gone through the IRB approval process. and stops.In each activities (marked in accelerometer plot),she In total,we have received 22 effective responses.The study performs several head gestures such as nodding,shaking,looking results are presented in Table I.Our survey results indicate up/down/left/right (sensor coordinate reference [20]). that nodding and shaking are popular and usually convey liminary observations from the collected trace in Fig.3:1)special meanings (e.g."yes"and "no").Circles are easy to Different activities add different amounts of noise.It is not perform since they are single-stroke.The rectangle and triangle easy to derive a general criterion for gesture detection in all gestures are the least favored,due to the multiple strokes they of the many kinds of activities the user may be participating entail.Simple"look up/down/left/right"gestures are easy and in at the time the gesture is made.2)Head gestures mainly fast,but they appear frequently in daily head movement as consist of rotations rather than accelerations.We see obvious shown in Fig.4,another study we have done to understand