User identification based on the dynamic features extracted from handwriting on touchscreen devices

Abstract

This research presents a methodology for user identification using ten English words handwritten on smartphone and mini-tablet. This research considers three features, namely Signature Precision (SP), Finger Pressure (FP), and Movement Time (MT) that were extracted from each of the ten English words using dynamic time warping. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbor classifier. We concluded that the best identification accuracy results from the combinations of the SP and the FP features with average accuracies of 74.55% and 69% were achieved on small smartphone and Minitablet respectively using a dataset of 42 users.