Smartphone User Identity Verification Using Gait Characteristics
Smartphone-based biometrics offers a wide range of possible solutions, which could be used to authenticate users and thus to provide an extra level of security and theft prevention. We propose a method for positive identification of smartphone user's identity using user's gait characteristics captured by embedded smartphone sensors (gyroscopes, accelerometers). The method is based on the application of the Random Projections method for feature dimensionality reduction to just two dimensions.
... two dimensions. Then, a probability distribution function (PDF) of derived features is calculated, which is compared against known user PDF. The Jaccard distance is used to evaluate distance between two distributions, and the decision is taken based on thresholding. The results for subject recognition are at an acceptable level: we have achieved a grand mean Equal Error Rate (ERR) for subject identification of 5.7% (using the USC-HAD dataset). Our findings represent a step towards improving the performance of gait-based user identity verification technologies. 2 of 20 or puzzles for different systems or devices (one for each device), it is a significant overhead to users when trying to remember each password. In practice, users often use the same password for different applications and choose easy to remember passwords such as those that contain semantic information like birthdays, family names, pet names, etc. In order to use different and random looking passwords users usually write down their passwords. As a result, the search space for an attacker is decreased. These practices make the password based authentication mechanisms vulnerable to dictionary attacks. Furthermore, entering a password may be time-consuming, error-prone and cumbersome, especially while using the phone on the go, and that is why many users are not using passwords. Drawing a graphical pattern on screen may reduce the burden, but it still requires explicit user interaction and is not very convenient in high mobility scenarios. Another target group of users who find passwords difficult are people suffering from memory loss or hand tremor. Finally, passwords are only artificially associated with users and cannot truly verify the identity of individuals. Consequently, they can be spied upon, guessed, lost or stolen, resulting in impersonation attacks and other security breaches. As a result, a significant part of users consider the password/PIN-based authentication as inconvenient and do not use it (see the results of a survey presented in  ). According to a survey presented in  , only 13% of the participants secure their phones with a PIN or visual code during standby, or deactivate the authentication methods of their mobile devices citing usability issues as the main reason for it. Other authentication methods such as face recognition, speech recognition or fingerprint scans are not widely used. For face recognition, the main concerns are the restricted memory and computational power available, as well as the uncontrolled ambient environment. For a continuous authentication, the speech during phone calls is analyzed and the authentication is performed in the background, which also introduces considerable computational overhead and reduces battery life. Fingerprint scanning requires an extra high-cost sensor that is not needed by the average end-user. Capturing high-quality finger photos for fingerprint recognition using existing phone cameras is still a problem for current mobile phones.