Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored password s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semi-controlled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process: sub-activity recognition followed by user impostor identification. Prior to the identification; Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrate that together time and frequency domain features optimized by ESP to train the wavelet kernel based extreme learning machine classifier is an effective system to identify the legitimate user or an impostor with (97 ) accuracy.
In recent years, several other implicit identification approaches have been proposed leveraging smartphone's sensor devices such as accelerometer @cite_38 , touch screen @cite_44 , GPS @cite_19 and microphone @cite_23 . T. Feng, et al @cite_15 proposed to extract finger motion speed and acceleration of touch patterns as features. Luca, et al @cite_18 suggested to directly compute the distance between pattern traces using the dynamic time warping algorithm. Sae-Bae, et al @cite_36 present 22 special touch patterns for user identification, most of which involve all five fingers simultaneously. They then computed dynamic time warping distance and Freshet distance between multi-touch traces. Frank, et al @cite_2 studied the correlation between 22 analytic features from touch traces and classified them using k nearest neighbors and support vector machines. Shahzad, et al @cite_16 explained the use of touchscreen patterns as a secure unlocking mechanism at the login screen.
@cite_38 @cite_18 @cite_36 @cite_44 @cite_19 @cite_23 @cite_2 @cite_15 @cite_16
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Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7 ) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10 , 18 and 19 , respectively. We propose a new behavioral biometric modality based on multi-touch gestures. We define a canonical set of multi-touch gestures based on the movement characteristics of the palm and fingertips being used to perform the gesture. We developed an algorithm to generate and verify multi-touch gesture templates. We tested our techniques on a set of 22 different gestures. Employing a matching algorithm for a multi-touch verification system with a k-NN classifier we achieved 1.28 Equal Error Rate (EER). With score-based classifiers where only the first five samples of a genuine subject were considered as templates, we achieved 4.46 EER. Further, with the combination of three commonly used gestures: pinch, zoom, and rotate, using all five fingers, 1.58 EER was achieved using a score-based classifier. These results are encouraging and point to the possibility of touch based biometric systems in real world applications like user verification and active authentication. Mobile devices are more and more integrated in workflows, especially when interacting with stationary resources like machines in order to improve productivity or usability, but risk unauthorized access or unwanted unattended operation. Systems for location based access control have been developed to restrict the user to be in specific locations in order to proceed in a workflow. However, these approaches do not consider the movement pattern of a user nor do they distinguish the severity of false-positives that might arise from imperfect location measurements which is crucial in certain workflows. In this paper, focusing on mobile users interacting with stationary machines, an approach for workflow policies is presented using three types of location constraints to enforce movement patterns. The evaluation of these constraints is based on a user’s location history which is generated in a tamper-proof environment on his mobile device and describes his geographical trajectory for a given timespan. The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20 . Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent post-login authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66 and False Reject Rate of 0.13 for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users.