Long-term influence of user identification based on touch operation on smart phone
Yuji Watanabe, Liu Kun · Procedia Computer Science · 2017
In our previous study, we collected a touch operations history when 40 subjects performed basic operation, text browsing, and web browsing using our Android application. From the touch history, we extracted 8 or 16 features for 6 gestures of swipe and pinch, and then identified subjects using some machine learning algorithms. The results showed that user identification rate reached about 95% for basic operation and text browsing. However, we used only one day touch history for each subject, so that a long-term influence when each subject performs the touch operations many times for a long period has been unclear. In this study, we record 10 touch operations histories of 11 subjects for a half year using the Android application to examine the long-term changes of user identification rate. The results show that the correctly classified rates for pinch gestures and swipe from down to up during simple text browsing are almost constant for a long term while the accuracy for swipe gesture in web browsing drops by about 10% as the number of experiments increases.