Context-Aware Mobile Biometric Authentication based on Support Vector Machines

The ubiquitous use of smartphones raises the need for stronger device protection. Traditional authentication methods on mobile devices are still knowledge-based, exhibiting well- known drawbacks. In addition, requests for passwords, PINs, or screen lock patterns represent an interruption of the device usage. In this paper a context-aware mobile biometric system is proposed. Modern smartphone devices comprise a multitude of sensors which can be utilized to measure a variety of environmental aspects, e.g. noise level or location. Based on this contextual information subject-specific context models are constructed in order to train SVMs, providing an alternative user-friendly authentication mechanism. In experiments a self- acquired database is employed where obtained results confirm the feasibility of the proposed system.