Keywords:Biometrics Signature Accelerometer Hidden Markov models Bayes Dynamic time warpingAs a result of advances in mobile technology, new services which benefit from the ubiquity of these devices are appearing. Some of these services require the identification of the subject since they may access private user information. In this paper, we propose to identify each user by drawing his/her handwritten signature in the air (in-air signature). In order to assess the feasibility of an in-air signature as a biometric feature, we have analysed the performance of several well-known pattern recognition techniques-Hidden Markov Models, Bayes classifiers and dynamic time warping-to cope with this problem. Each technique has been tested in the identification of the signatures of 96 individuals. Furthermore, the robustness of each method against spoofing attacks has also been analysed using six impostors who attempted to emulate every signature. The best results in both experiments have been reached by using a technique based on dynamic time warping which carries out the recognition by calculating distances to an average template extracted from several training instances. Finally, a permanence analysis has been carried out in order to assess the stability of in-air signature over time.

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3-D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.

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This paper presents a new approach to the problem of gesture recognition in real time using inexpensive accelerometers. This approach is based on the idea of creating specialized signal predictors for each gesture class. These signal predictors forecast future acceleration values from current ones. The errors between the measured acceleration of a given gesture and the predictors are used for classification. This approach is modular and allows for seamless inclusion of new gesture classes. These predictors are implemented using Continuous Time Recurrent Neural Networks (CTRNN). On the one hand, this kind of networks exhibits rich dynamical behaviour that is useful in gesture recognition and on the other, they have a relatively low computational cost that is interesting feature for real time systems.