Cell phone-based biometric identification

Abstract

Abstract-Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometric identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification.

Identifying user traits by mining smart phone accelerometer data

“…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.…”

“…Because of their portability and ubiquity, these smart phone sensors provide us with an opportunity to learn a great deal about their users. In prior work, for example, we used the smart phone's accelerometer to determine what physical activity (e.g., walking, jogging, sitting) a user is performing [10] and to identify/authenticate the user [11]. In this paper we extend this latter work on biometric identification to predict user characteristics, or traits.…”

Design considerations for the WISDM smart phone-based sensor mining architecture

“…Since traditional predictive data mining algorithms (e.g., decision trees) do not operate directly on time-series data, the next step for traditional methods involves transforming the time-series data into examples that summarize the data over a fixed time period. For our current activity recognition and biometric applications [9,10] we generate one example, with 43 features, from each 10 seconds of accelerometer data. Next, pre-built classifiers are used to generate predictions (our architecture also supports the dynamic creation of classifiers).…”

“…Such activity recognition applications can be used to determine if a user is getting enough physical activity or to customize smart phone behavior based on context. Since a user's movements form a distinctive signature, a smart phone's accelerometer can also be used for biometric identification and authentication [6,10]. Location-based sensor data mining is a particularly popular and expanding application area, which has matured sufficiently to spawn commercial applications.…”

“…Other applications include Google Maps and Navigator, which identify traffic based on real-time GPS location data from large numbers of smart-phone users, which is then combined with knowledge about historical traffic patterns. Our WISDM research group [17] has developed smart phone-based activity recognition [9] and biometric [10] applications and is in the process of implementing them within our WISDM architecture so that they operate in realtime. We have chosen to implement our WISDM sensor mining architecture using the Android mobile operating system and Android phones for reasons described in Section 3.1, but this architecture can be adapted to use other mobile operating systems.…”