Smart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), direction sensor (compass), audio sensor (microphone), image sensor (camera), proximity sensor, light sensor, and temperature sensor. Combined with the ubiquity and portability of these devices, these sensors provide us with an unprecedented view into people’s lives—and an excellent opportunity for data mining. But there are obstacles to sensor mining applications, due to the severe resource limitations (e.g., power, memory, bandwidth) faced by mobile devices. In this paper we discuss these limitations, their impact, and propose a solution based on our WISDM (WIireless Sensor Data Mining) smart phone-based sensor mining architecture.

Abstract—Biometric gait recognition based on accelerometer data is still a new field of research. It has the merit of offering an unobtrusive and hence user-friendly method for authentication on mobile phones. Most publications in this area are based on extracting cycles (two steps) from the gait data which are later used as features in the authentication process. In this paper the application of Hidden Markov Models is proposed instead. These have already been successfully implemented in speaker recognition systems. The advantage is that no error-prone cycle extraction has to be performed, but the accelerometer data can be directly used to construct the model and thus form the basis for successful recognition. Testing this method with accelerometer data of 48 subjects recorded using a commercial of the shelve mobile phone a false non match rate (FNMR) of 10.42 % at a false match rate (FMR) of 10.29 % was obtained. This is half of the error rate obtained when applying an advanced cycle extraction method to the same data set in previous work. Keywords-biometrics; gait recognition; hidden markov models; accelerometers; authentication on mobile devices I.

Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as ―soft biometrics‖ because these traits are not sufficiently distinctive or invariant to uniquely identify an individual—but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user’s weight when calculating calories burned).

In this paper we wish to propose an integrated model which uses a combination of Biometrics, smart card, user name, single interface and single password for accessing multiple bank accounts by the user in online banking applications. A variety of biometric systems are found in the literatures which are used for authentication purpose. In general, most of the users will have multiple online bank accounts and each one of them will have separate passwords. One has to remember all the passwords if he/she wants to operate his/her account. On the other hand if the user uses same password chances for cracking would increase. We propose a system where an interface is provided to the user to enter his details along with the biometric data. These data is sent to the authentication server which in turn allows the user to operate all his bank accounts with a onetime TAN generated by the server. This is an enhanced integrated system which provides a single interface for operating multiple bank accounts, uses smart card as a database to store the templates as well as encryption, hash function etc., and two servers namely Remote Authentication Server (RAS) and Remote Control Server (RCS) along with the mobile service provider. In addition to that we propose to use artificial intelligence on the RAS side for classification and identification of genuine users and fraudulent users.