We present SMAUG (Secure Mobile Authentication Using Gestures), a novel biometric assisted authentication algorithm for mobile devices that is solely based on data collected from multiple sensors that are usually installed on modern devices -- touch screen, gyroscope and accelerometer. As opposed to existing approaches, our system supports a fully flexible user input such as free-form gestures, multi-touch, and arbitrary amount of strokes. Our experiments confirm that this approach provides a high level of robustness and security. More precisely, in 77 of all our test cases over all gestures considered, a user has been correctly identified during the first authentication attempt and in 99 after the third attempt, while an attacker has been detected in 97 of all test cases. As an example, gestures that have a good balance between complexity and usability, e.g., drawing a two parallel lines using two fingers at the same time, 100 success rate after three login attempts and 97 impostor detection rate were given. We stress that we consider the strongest possible attacker model: an attacker is not only allowed to monitor the legitimate user during the authentication process, but also receives additional information on the biometric properties, for example pressure, speed, rotation, and acceleration. We see this method as a significant step beyond existing authentication methods that can be deployed directly to devices in use without the need of additional hardware.
With respect to gesture recognition for single-touch gestures, Rubine @cite_30 is the usual reference when comparing new single-touch algorithms. Another prominent example for single-touch and single-stroke gesture recognition is @cite_0 . The authors of @cite_36 present a very efficient follow-up work for single-touch and multi-stroke. In short, the algorithm joins all strokes in all possible combinations and reduces the gesture recognition problem therefore to the case of single-touch gestures. However, this algorithm family needs a predefined set of gestures. The authors in @cite_1 have developed a multi-dimension DTW for gesture recognition. In @cite_35 , the authors present a gesture based user authentication scheme for touch screens using solely the accelerometer. 3D hand gesture recognition in the air with mobile devices and accelerometer is examined in @cite_15 . Similar research was done for Kinect and gesture recognition in @cite_27 , also for Wii @cite_38 .
@cite_30 @cite_35 @cite_38 @cite_36 @cite_1 @cite_0 @cite_27 @cite_15
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Gesture-Based interfaces offer an alternative to traditional keyboard, menu, and direct manipulation interfaces. The ability to specify objects, an operation, and additional parameters with a single intuitive gesture appeals to both novice and experienced users. Unfortunately, gesture-based interfaces have not been extensively researched, partly because they are difficult to create. This paper describes GRANDMA, a toolkit for rapidly adding gestures to direct manipulation interfaces. The trainable single-stroke gesture recognizer used by GRANDMA is also described. With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password PIN pattern screen when reactivated. Passwords PINs patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. Furthermore, passwords PINs patterns are inconvenient for users to enter frequently. In this paper, we propose GEAT, a gesture based user authentication scheme for the secure unlocking of touch screen devices. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time. Even if attackers see what gesture a user performs, they cannot reproduce the behavior of the user doing gestures through shoulder surfing or smudge attacks. We implemented GEAT on Samsung Focus running Windows, collected 15009 gesture samples from 50 volunteers, and conducted real-world experiments to evaluate GEAT's performance. Experimental results show that our scheme achieves an average equal error rate of 0.5 with 3 gestures using only 25 training samples. The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6 accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures. Prior work introduced @math 1 recognizer. @math 1 before it. Since then, Protractor has been introduced, a unistroke pen and finger gesture recognition algorithm also based on template-matching and @math 1. This paper presents work to streamline @math N, and negligibly less accurate (<0.2 ). We also discuss the impact that the number of templates, the input speed, and input method (e.g., pen vs. finger) have on recognition accuracy, and examine the most confusable gestures. Although mobile, tablet, large display, and tabletop computers increasingly present opportunities for using pen, finger, and wand gestures in user interfaces, implementing gesture recognition largely has been the privilege of pattern matching experts, not user interface prototypers. Although some user interface libraries and toolkits offer gesture recognizers, such infrastructure is often unavailable in design-oriented environments like Flash, scripting environments like JavaScript, or brand new off-desktop prototyping environments. To enable novice programmers to incorporate gestures into their UI prototypes, we present a " @math 1 recognizer, Dynamic Time Warping, and the Rubine classifier on user-supplied gestures, we found that @math 1 recognizer to aid development, inspection, extension, and testing. Password-based authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. In this paper, we propose a user-friendly authentication system (KinWrite) that allows users to choose arbitrary, short and easy-to-memorize passwords while providing resilience to password cracking and password theft. KinWrite lets users write their passwords in 3D space and captures the handwriting motion using a low cost motion input sensing device—Kinect. The low resolution and noisy data captured by Kinect, combined with low consistency of in-space handwriting, have made it challenging to verify users. To overcome these challenges, we exploit the Dynamic Time Warping (DTW) algorithm to quantify similarities between handwritten passwords. Our experimental results involving 35 signatures from 18 subjects and a brute-force attacker have shown that KinWrite can achieve a 100 precision and a 70 recall (the worst case) for verifying honest users, encouraging us to carry out a much larger scale study towards designing a foolproof system.