Statistical approaches for personal feature extraction from pressure array sensors

We propose two statistical probability approaches to extract personal feature from the user´s grip force data. One approach is based on grip force changes predicted by the Kalman filter, the other is based on distributions of grip force changes by Jensen-Shannon(JS) divergence. Personal feature is the customary behavior that repeatedly appears without the user being aware of it. The personal feature is used for not only user-authentication, but also user-special commands. We mount pressure array sensors on a mobile phone and show that our proposals can extract personal feature from the user´s grip force data with 10[%] error in FAR-FRR by the Kalman filter approach and the accuracy of 100[%] by the JS divergence approach.